**ClassifyR** Developer's Guide
Dario Strbenac,
Ellis Patrick
The University of Sydney, Australia.
DevelopersGuide.Rmd
Introduction
ClassifyR is regularly maintained and new functionality is often added. This may be done by users of ClassifyR. This guide will summarise the technical concepts of how ClassifyR works and assist users to contribute new functionality to the package by understanding the design principles of ClassifyR. Almost all functions in ClassifyR are S4 methods, which has the added benefit of checking that the type of input data matches what is required. Basic variables in R have no strict type, unlike most other programming languages.
New Model Building Function Requirements
Input Data Types
Data used for predictive modelling can take many shapes and forms. ClassifyR will ensure that any of the three valid input types of matrix, DataFrame and MultiAssayExperiment will be a DataFrame before it is sent to a modelling function.
Importantly, ClassifyR implements a number of methods for classification using different kinds of changes in measurements between classes. Most classifiers work with features where the means are different. In addition to changes in means (DM), ClassifyR also allows for classification using differential variability (DV; changes in scale) and differential distribution (DD; changes in location and/or scale).
Registering the Function
ClassifyR keeps track of functions used for model
building, to allow automated naming of axes labels or tick marks in a
nice format. To do this, each function has a pretty name associated with
it in the file constants.R. Firstly, find
.ClassifyRenvir[["functionsTable"]]
in that file, which is
basically a two-column matrix stored in an environment and add your new
function and its pretty name as an additional row of that matrix.
Secondly, there is a convenience cross-validation function named
crossValidate
. It allows convenient specification of
feature selection and modelling combinations with keywords. To add a new
feature ranking method, add a new entry to
.selectionKeywordToFunction
in utilities.R
by
adding a new keyword and function pair. There is a similar statement in
the same R file for modelling functions, in which a new classifier, for
instance, would be added.
Documenting the Function
Firstly, in the main vignette, there are a couple of tables summarising the feature selection and model building functions available. Add your function to the relevant table and put a tick mark in the appropriate column(s) to summarise what kind of analysis it offers.
Secondly, there is a NEWS
file in inst
folder. Add a new entry for the new functionality.
Incorprating Changes into the Package
Please make a pull request at ClassifyR’s GitHub
website. Your code will be reviewed and you will be added as a
contributor the the DESCRIPTION
file of the package.
Feature Ranking and Selection
The various functions in ClassifyR for prioritising
features return a ranking, from best to worst, of features because the
package takes care of the actual feature selection process. Based on the
specification of the range of values of top features to try, the set of
variables with either the best resubstitution (default) or nested
cross-validation performance is found by the private function
.doSelection
.
The input data may be one table or a collection of tables. The
DataFrame
variable type stores metadata about the variables
in its columns. Therefore, the ranking function simply returns a numeric
indices of the features ranked from best to worst.
Extractor Functions
Sometimes, feature ranking / selection is not done before a model is
trained. Also, some trained models do implicit feature selection, such
as random forest. If a new modelling function has implicit feature
selection, it also needs to be accompanied by a function to extract the
selected features. See forestFeatures
in
interfaceRandomForest.R
for one such example. The return
value of such a function needs to be a list of length 2. The first
element has all of the features, ranked from best to worst. The second
element has indicies corresponding only to the selected features.
In the random forest example, the feature ranking is based on the
variable importance score calculated by the random forest package
(i.e. randomForest::importance(forest)
) and the selection
is based on the non-zero occurrence of a variable in the forest
(i.e. randomForest::varUsed(forest) > 0
). These two
vectors are returned in a list for use by
ClassifyR.
New Model Evaluation Function Requirements
The function must accept a list of ClassifyResult
elements, the container class which stores all of the useful information
about a cross-validation after it has completed. For accessors to use to
access class slots, please see ?ClassifyResult
.
Coding Style
To help maintain consistency with the existing code, please:
- Use
camelCase
coding style for variable names andCamelCase
for class names. - Use vectorised loops such as
lapply
andmapply
. Don’t usefor
loops. - Use
<-
for variable assignment rater than=
. - Follow the other style requirements of Bioconductor.