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---
title: "CPOs Built Into mlrCPO"
author: "Martin Binder"
date: "`r Sys.Date()`"
output: rmarkdown::html_vignette
vignette: >
%\VignetteIndexEntry{3. Builtin CPOs}
%\VignetteEngine{knitr::rmarkdown}
\usepackage[utf8]{inputenc}
---
```{r, eval = TRUE, child = 'toc/vignettetoc.Rmd'}
```
```{r, echo = FALSE}
library("mlrCPO")
```
## Listing CPOs
Builtin CPOs can be listed with `listCPO()`.
```{r, eval = FALSE}
listCPO()[, c("name", "category", "subcategory")]
```
```{r, echo = FALSE, results = 'asis'}
tab = listCPO()[, c("name", "category", "subcategory")]
owncontent = readLines(path)
headlines = grep("^#+ +", owncontent, value = TRUE)
headlines = gsub("^#+ +", "", headlines)
tab[[1]] = sapply(tab[[1]], function(x)
if (x %in% headlines) sprintf("[%s](#%s)", x, tolower(x)) else x)
knitr::kable(tab, "html")
```
## NULLCPO
`NULLCPO` is the neutral element of `%>>%`. It is returned by some functions when no other CPO or Retrafo is present.
```{r}
NULLCPO
is.nullcpo(NULLCPO)
NULLCPO %>>% cpoScale()
NULLCPO %>>% NULLCPO
print(as.list(NULLCPO))
pipeCPO(list())
```
## 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.
```{r}
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"))
```
### cpoMultiplex
Combine many CPOs into one, with an extra `selected.cpo` parameter that chooses between them.
```{r}
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"))
```
### 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.
```{r}
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)
```
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.
```{r}
scale = cpoScale(id = "scale")
scale.pca = scale %>>% cpoPca()
cbinder = cpoCbind(scaled = scale, pcad = scale.pca, original = NULLCPO)
```
```{r}
# 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)
head(iris %>>% cbinder)
```
```{r}
# 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
head(iris %>>% cbinder)
```
```{r}
# alternatively, we apply the cbinder only to numerical data
head(iris %>>% cpoWrap(cbinder, affect.type = "numeric"))
```
### 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.
```{r}
cpo = cpoTransformParams(cpoPca(), alist(pca.scale = pca.center))
retr = pid.task %>|% setHyperPars(cpo, pca.center = FALSE)
getCPOTrainedState(retr)$control # both 'center' and 'scale' are FALSE
```
```{r}
mplx = cpoMultiplex(list(cpoIca(export = "n.comp"), cpoPca(export = "rank")))
!mplx
mtx = cpoTransformParams(mplx, alist(ica.n.comp = comp, pca.rank = comp),
pSS(comp: integer[1, ]), list(comp = 1))
head(iris %>>% setHyperPars(mtx, selected.cpo = "ica", comp = 2))
head(iris %>>% setHyperPars(mtx, selected.cpo = "pca", comp = 3))
```
## Data Manipulation
### cpoScale
Implements the `base::scale` function.
```{r}
df = data.frame(a = 1:3, b = -(1:3) * 10)
df %>>% cpoScale()
df %>>% cpoScale(scale = FALSE) # center = TRUE
```
### cpoPca
Implements `stats::prcomp`. No scaling or centering is performed.
```{r}
df %>>% cpoPca()
```
### cpoDummyEncode
Dummy encoding of factorial variables. Optionally uses the first factor as reference variable.
```{r}
head(iris %>>% cpoDummyEncode())
head(iris %>>% cpoDummyEncode(reference.cat = TRUE))
```
### cpoSelect
Select to use only certain columns of a dataset. Select by column index, name, or regex pattern.
```{r}
head(iris %>>% cpoSelect(pattern = "Width"))
# selection is additive
head(iris %>>% cpoSelect(pattern = "Width", type = "factor"))
```
### cpoDropConstants
Drops constant features or numerics, with variable tolerance
```{r}
head(iris) %>>% cpoDropConstants() # drops 'species'
head(iris) %>>% cpoDropConstants(abs.tol = 0.2) # also drops 'Petal.Width'
```
### cpoFixFactors
Drops unused factors and makes sure prediction data has the same factor levels as training data.
```{r}
levels(iris$Species)
```
```{r}
irisfix = head(iris) %>>% cpoFixFactors() # Species only has level 'setosa' in train
levels(irisfix$Species)
```
```{r}
rf = retrafo(irisfix)
iris[c(1, 100, 140), ]
iris[c(1, 100, 140), ] %>>% rf
```
### cpoMissingIndicators
Creates columns indicating missing data. Most useful in combination with cpoCbind.
```{r}
impdata = df
impdata[[1]][1] = NA
impdata
```
```{r}
impdata %>>% cpoMissingIndicators()
impdata %>>% cpoCbind(NULLCPO, dummy = cpoMissingIndicators())
```
### cpoApplyFun
Apply an univariate function to data columns
```{r}
head(iris %>>% cpoApplyFun(function(x) sqrt(x) - 10, affect.type = "numeric"))
```
### cpoAsNumeric
Convert (non-numeric) features to numeric
```{r, echo = FALSE}
set.seed(123)
```
```{r}
head(iris[sample(nrow(iris), 10), ] %>>% cpoAsNumeric())
```
### cpoCollapseFact
Combine low prevalence factors. Set `max.collapsed.class.prevalence` how big the combined factor level may be.
```{r}
iris2 = iris
iris2$Species = factor(c("a", "b", "c", "b", "b", "c", "b", "c",
as.character(iris2$Species[-(1:8)])))
head(iris2, 10)
head(iris2 %>>% cpoCollapseFact(max.collapsed.class.prevalence = 0.2), 10)
```
### cpoModelMatrix
Specify which columns get used, and how they are transformed, using a `formula`.
```{r}
head(iris %>>% cpoModelMatrix(~0 + Species:Petal.Width))
# use . + ... to retain originals
head(iris %>>% cpoModelMatrix(~0 + . + Species:Petal.Width))
```
### cpoScaleRange
scale values to a given range
```{r}
head(iris %>>% cpoScaleRange(-1, 1))
```
### cpoScaleMaxAbs
Multiply features to set the maximum absolute value.
```{r}
head(iris %>>% cpoScaleMaxAbs(0.1))
```
### cpoSpatialSign
Normalize values row-wise
```{r}
head(iris %>>% cpoSpatialSign())
```
## 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.
```{r}
impdata %>>% cpoImpute(cols = list(a = imputeMedian()))
```
```{r, error = TRUE}
impdata %>>% cpoImpute(cols = list(b = imputeMedian())) # NAs remain
impdata %>>% cpoImputeAll(cols = list(b = imputeMedian())) # error, since NAs remain
```
```{r, error = TRUE}
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)
```
```{r}
# instead, the following works:
train(cpoImputeAll(cols = list(a = imputeMedian())) %>>% makeLearner("regr.lm"), missing.task)
```
#### Specialised Imputation Wrappers
There is one for each imputation method.
```{r}
impdata %>>% cpoImputeConstant(10)
getTaskData(missing.task %>>% cpoImputeMedian())
# The specialised impute CPOs are:
listCPO()[listCPO()$category == "imputation" & listCPO()$subcategory == "specialised",
c("name", "description")]
```
## Feature Filtering
There is one *general* and many *specialised* feature filtering CPOs. The general filtering CPO, `cpoFilterFeatures`, is a thin wrapper around `filterFeatures` and takes the filtering method as its argument. The specialised CPOs each call a specific filtering method.
Most arguments of `filterFeatures` are reflected in the CPOs. The exceptions being:
1. for `filterFeatures`, the filter method arguments are given in a list `filter.args`, instead of in `...`
2. The argument `fval` was dropped for the specialised filter CPOs.
3. The argument `mandatory.feat` was dropped. Use `affect.*` parameters to prevent features from being filtered.
```{r}
head(getTaskData(iris.task %>>% cpoFilterFeatures(method = "variance", perc = 0.5)))
head(getTaskData(iris.task %>>% cpoFilterVariance(perc = 0.5)))
# The specialised filter CPOs are:
listCPO()[listCPO()$category == "featurefilter" & listCPO()$subcategory == "specialised",
c("name", "description")]
```
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