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title: "Integrating Another Filter Method"
output: rmarkdown::html_vignette
vignette: >
```{r, echo = FALSE, message=FALSE}
# show grouped code output instead of single lines
knitr::opts_chunk$set(collapse = TRUE)
A lot of feature filter methods are already integrated in `mlr` and a complete list is given in the [Appendix](filter_methods.html){target="_blank"} or can be obtained using `listFilterMethods()`.
You can easily add another filter, be it a brand new one or a method which is already implemented in another package, via function `makeFilter()`.
# Filter objects
In `mlr` all filter methods are objects of class Filter (`makeFilter()`) and are registered in an environment called `.FilterRegister` (where `listFilterMethods()` looks them up to compile the list of available methods).
To get to know their structure let's have a closer look at the `"rank.correlation"` filter.
```{r eval = FALSE}
filters = as.list(mlr:::.FilterRegister)
## Filter: 'rank.correlation'
## Packages: ''
## Supported tasks: regr
## Supported features: numerics
## List of 6
## $ name : chr "rank.correlation"
## $ desc : chr "Spearman's correlation between feature and target"
## $ pkg : chr(0)
## $ supported.tasks : chr "regr"
## $ supported.features: chr "numerics"
## $ fun :function (task, nselect, ...)
## ..- attr(*, "srcref")= 'srcref' int [1:8] 325 9 328 3 9 3 2308 2311
## .. ..- attr(*, "srcfile")=Classes 'srcfilealias', 'srcfile' <environment: 0x55db8ddf6dc0>
## - attr(*, "class")= chr "Filter"
## function(task, nselect, ...) {
## data = getTaskData(task, target.extra = TRUE)
## abs(cor(as.matrix(data$data), data$target, use = "pairwise.complete.obs", method = "spearman")[, 1L])
## }
## <bytecode: 0x55db8dc9fe00>
## <environment: namespace:mlr>
The core element is `$fun` which calculates the feature importance.
For the `"rank.correlation"` filter it just extracts the data and formula from the `task` and passes them on to the `base::cor()` function.
Additionally, each Filter (`makeFilter()`) object has a `$name`, which should be short and is for example used to annotate graphics (cp. `plotFilterValues()`), and a slightly more detailed description in slot `$desc`.
If the filter method is implemented by another package its name is given in the `$pkg` member.
Moreover, the supported task types and feature types are listed.
# Writing a new filter method
You can integrate your own filter method using `makeFilter()`.
This function generates a Filter (`makeFilter()`) object and also registers it in the `.FilterRegister` environment.
The arguments of `makeFilter()` correspond to the slot names of the Filter (`makeFilter()`) object above.
Currently, feature filtering is only supported for supervised learning tasks and possible values for `supported.tasks` are `"regr"`, `"classif"` and `"surv"`.
`supported.features` can be `"numerics"`, `"factors"` and `"ordered"`.
`fun` must be a function with at least the following formal arguments:
* `task` is a `mlr` learning `Task()`.
* `nselect` corresponds to the argument of `generateFilterValuesData()` of the same name and specifies the number of features for which to calculate importance scores.
Some filter methods have the option to stop after a certain number of top-ranked features have been found in order to save time and ressources when the number of features is high.
The majority of filter methods integrated in `mlr` doesn't support this and thus `nselect` is ignored in most cases.
An exception is the minimum redundancy maximum relevance filter from package `mRMRe`.
* `...` for additional arguments.
`fun` must return a named vector of feature importance values.
By convention the most important features receive the highest scores.
If you are making use of the `nselect` option `fun` can either return a vector of `nselect` scores or a vector as long as the total numbers of features in the task filled with `NAs` for all features whose scores weren't calculated.
When writing `fun` many of the getter functions for `Task()`s come in handy,
particularly `getTaskData()`, `getTaskFormula()` and `getTaskFeatureNames()`.
It's worth having a closer look at `getTaskData()` which provides many options for
formatting the data and recoding the target variable.
As a short demonstration we write a totally meaningless filter that determines the
importance of features according to alphabetical order, i.e., giving highest scores to features with names that come first (`decreasing = TRUE`) or last (`decreasing = FALSE`) in the alphabet.
```{r, cache = FALSE}
name = "nonsense.filter",
desc = "Calculates scores according to alphabetical order of features",
pkg = character(0),
supported.tasks = c("classif", "regr", "surv"),
supported.features = c("numerics", "factors", "ordered"),
fun = function(task, nselect, decreasing = TRUE, ...) {
feats = getTaskFeatureNames(task)
imp = order(feats, decreasing = decreasing)
names(imp) = feats
The `nonsense.filter` is now registered in `mlr` and shown by `listFilterMethods()`.
You can use it like any other filter method already integrated in `mlr` (i.e., via the `method` argument of `generateFilterValuesData()` or the `fw.method` argument of
`makeFilterWrapper()`; see also the page on [feature selection](feature_selection.html){target="_blank"}.
d = generateFilterValuesData(iris.task, method = c("nonsense.filter", "anova.test"))
iris.task.filtered = filterFeatures(iris.task, method = "nonsense.filter", abs = 2)
You might also want to have a look at the [source code]( of the filter methods already integrated in `mlr` for some more complex and meaningful examples.
```{r, echo = FALSE}
rm("nonsense.filter", envir = mlr:::.FilterRegister)
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