Builtin CPOs can be listed with
NULLCPO is the neutral element of
%>>%. It is returned by some functions when no other CPO or Retrafo is present.
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) head(iris %>>% setHyperPars(cpa, wrap.cpo = cpoScale())) head(iris %>>% setHyperPars(cpa, wrap.cpo = cpoPca())) # 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"))
Combine many CPOs into one, with an extra
selected.cpo parameter that chooses between them.
cpm = cpoMultiplex(list(cpoScale, cpoPca)) print(cpm, verbose = TRUE) head(iris %>>% setHyperPars(cpm, selected.cpo = "scale")) # every CPO's Hyperparameters are exported head(iris %>>% setHyperPars(cpm, selected.cpo = "scale", scale.center = FALSE)) head(iris %>>% setHyperPars(cpm, selected.cpo = "pca"))
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
requires of the involved parameters are not adjusted, since this is impossible in principle.
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
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.
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
ParamHelpers, but it can also be used to set multiple parameters at the same time, depending on a single new parameter.
mplx = cpoMultiplex(list(cpoIca(export = "n.comp"), cpoPca(export = "rank"))) !mplx 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)) head(iris %>>% setHyperPars(mtx, selected.cpo = "pca", comp = 3))
stats::prcomp. No scaling or centering is performed.
Dummy encoding of factorial variables. Optionally uses the first factor as reference variable.
Select to use only certain columns of a dataset. Select by column index, name, or regex pattern.
Drops constant features or numerics, with variable tolerance
Drops unused factors and makes sure prediction data has the same factor levels as training data.
Creates columns indicating missing data. Most useful in combination with cpoCbind.
Apply an univariate function to data columns
Convert (non-numeric) features to numeric
Combine low prevalence factors. Set
max.collapsed.class.prevalence how big the combined factor level may be.
Specify which columns get used, and how they are transformed, using a
scale values to a given range
Multiply features to set the maximum absolute value.
Normalize values row-wise
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
reimpute() functions. The specialised imputation CPOs each implement exactly one imputation method and are closer to the behaviour of typical CPOs.
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.
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)
There is one for each imputation method.
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.