mlrCPO: Composable Preprocessing Operators for mlr

GSoC 2017 Project: Operator Based Machine Learning Pipeline Construction

What is CPO?

> task = iris.task
> task %<>>% cpoScale(scale = FALSE) %>>% cpoPca() %>>%  # pca
>   cpoFilterChiSquared(abs = 3) %>>%  # filter
>   cpoModelMatrix(~ 0 + .^2)  # interactions
> head(getTaskData(task))
        PC1        PC2         PC3    PC1:PC2     PC1:PC3      PC2:PC3 Species
1 -2.684126 -0.3193972  0.02791483  0.8573023 -0.07492690 -0.008915919  setosa
2 -2.714142  0.1770012  0.21046427 -0.4804064 -0.57122986  0.037252434  setosa
3 -2.888991  0.1449494 -0.01790026 -0.4187575  0.05171367 -0.002594632  setosa
4 -2.745343  0.3182990 -0.03155937 -0.8738398  0.08664130 -0.010045316  setosa
5 -2.728717 -0.3267545 -0.09007924  0.8916204  0.24580071  0.029433798  setosa
6 -2.280860 -0.7413304 -0.16867766  1.6908707  0.38473006  0.125045884  setosa

"Composable Preprocessing Operators" are an extension for the mlr ("Machine Learning in R") project which represent preprocessing operations (e.g. imputation or PCA) in the form of R objects. These CPO objects can be composed to form more complex operations, they can be applied to data sets, and they can be attached to mlr Learner objects to generate complex machine learning pipelines that perform both preprocessing and model fitting.

Table of Contents

• Short Overview
• Installation
• Documentation
• Project Status
• Similar Projects
• License

Short Overview

CPOs are created by calling a constructor.

> cpoScale()
scale(center = TRUE, scale = TRUE)

The created objects have Hyperparameters that can be manipulated using getHyperPars, setHyperPars etc, just like in mlr.

> getHyperPars(cpoScale())
$scale.center
[1] TRUE

$scale.scale
[1] TRUE

> setHyperPars(cpoScale(), scale.center = FALSE)
scale(center = FALSE, scale = TRUE)

The %>>%-operator can be used to create complex pipelines.

> cpoScale() %>>% cpoPca()
(scale >> pca)(scale.center = TRUE, scale.scale = TRUE)

This operator can also be used to apply an operation to a data set:

> head(iris %>>% cpoPca())
  Species       PC1      PC2          PC3          PC4
1  setosa -5.912747 2.302033  0.007401536  0.003087706
2  setosa -5.572482 1.971826  0.244592251  0.097552888
3  setosa -5.446977 2.095206  0.015029262  0.018013331
4  setosa -5.436459 1.870382  0.020504880 -0.078491501
5  setosa -5.875645 2.328290 -0.110338269 -0.060719326
6  setosa -6.477598 2.324650 -0.237202487 -0.021419633

Or to attach an operation to an MLR Learner, which extends the Learner's hyperparameters by the CPO's hyperparameters:

> cpoScale() %>>% makeLearner("classif.logreg")
Learner classif.logreg.scale from package stats
Type: classif
Name: ; Short name: 
Class: CPOLearner
Properties: numerics,factors,prob,twoclass
Predict-Type: response
Hyperparameters: model=FALSE,scale.center=TRUE,scale.scale=TRUE

Get a list of all CPOs by calling listCPO().

Installation

Install mlrCPO from CRAN, or use the more recent GitHub version:

devtools::install_github("mlr-org/mlrCPO")

Documentation

To effectively use mlrCPO, you should first familiarize yourself a little with mlr. There is an extensive tutorial online; for more ressources on mlr, see the overview on mlr's GitHub page.

To get familiar with mlrCPO, it is recommended that you read the vignettes. For each vignette, there is also a compact version that has all the R output removed.

1. First Steps: Introduction and short overview (compact version).
2. mlrCPO Core: Description of general tools for CPO handling (compact version).
3. Builtin CPOs: Listing and description of all builtin CPOs (compact version).
4. Custom CPOs: How to create your own CPOs. (compact version).
5. CPO Internals: A small intro guide for developers into the code base. See the info directory for pdf / html versions.

For more documentation of individual mlrCPO functions, use R's built-in help() functionality.

Project Status

The foundation of mlrCPO is built and is reasonably stable, only small improvements and stability fixes are expected here. There are still many concrete implementations of preprocessing operators to be written.

Similar Projects

There are other projects that provide functionality similar to mlrCPO for other machine learning frameworks. The caret project provides some preprocessing functionality, though not as flexible as mlrCPO. dplyr has similar syntax and some overlapping functionality, but is focused ultimately more on (manual) data manipulation instead of (machine learning pipeline integrated) preprocessing. Much more close to mlrCPO's functionality is the Recipes package. scikit learn also has preprocessing functionality built in.

License

The BSD 2-Clause License