mlr3

Package website: release, dev

Efficient, object-oriented programming on the building blocks of machine
learning. Successor of mlr.

Resources

• We started writing a book, but it
  is still in early stages.
• Reference Manual
• Extension
  packages
• mlr-outreach contains
  talks and slides
• Blog about mlr and mlr3
• Wiki

Installation

Install the last release from CRAN:

install.packages("mlr3")

Install the development version from GitHub:

remotes::install_github("mlr-org/mlr3")

Example

Constructing Learners and Tasks

library(mlr3)

# create learning task
task_iris = TaskClassif$new(id = "iris", backend = iris, target = "Species")
task_iris

## <TaskClassif:iris> (150 x 5)
## * Target: Species
## * Properties: multiclass
## * Features (4):
##   - dbl (4): Petal.Length, Petal.Width, Sepal.Length, Sepal.Width

# load learner and set hyperparameter
learner = lrn("classif.rpart", cp = 0.01)

Basic train + predict

# train/test split
train_set = sample(task_iris$nrow, 0.8 * task_iris$nrow)
test_set = setdiff(seq_len(task_iris$nrow), train_set)

# train the model
learner$train(task_iris, row_ids = train_set)

# predict data
prediction = learner$predict(task_iris, row_ids = test_set)

# calculate performance
prediction$confusion

##             truth
## response     setosa versicolor virginica
##   setosa         11          0         0
##   versicolor      0         12         1
##   virginica       0          0         6

measure = msr("classif.acc")
prediction$score(measure)

## classif.acc 
##   0.9666667

Resample

# automatic resampling
resampling = rsmp("cv", folds = 3L)
rr = resample(task_iris, learner, resampling)
rr$score(measure)

##             task task_id               learner    learner_id     resampling
## 1: <TaskClassif>    iris <LearnerClassifRpart> classif.rpart <ResamplingCV>
## 2: <TaskClassif>    iris <LearnerClassifRpart> classif.rpart <ResamplingCV>
## 3: <TaskClassif>    iris <LearnerClassifRpart> classif.rpart <ResamplingCV>
##    resampling_id iteration prediction classif.acc
## 1:            cv         1     <list>        0.92
## 2:            cv         2     <list>        0.92
## 3:            cv         3     <list>        0.94

rr$aggregate(measure)

## classif.acc 
##   0.9266667

Why a rewrite?

mlr was first released to
CRAN in 2013. Its core design
and architecture date back even further. The addition of many features
has led to a feature
creep which makes
mlr hard to maintain and hard to
extend. We also think that while mlr was nicely extensible in some parts
(learners, measures, etc.), other parts were less easy to extend from
the outside. Also, many helpful R libraries did not exist at the time
mlr was created, and their inclusion
would result in non-trivial API changes.

Design principles

• Only the basic building blocks for machine learning are implemented
  in this package.
• Focus on computation here. No visualization or other stuff. That can
  go in extra packages.
• Overcome the limitations of R’s S3
  classes with the help of
  R6.
• Embrace R6 for a clean
  OO-design, object state-changes and reference semantics. This might
  be less “traditional R”, but seems to fit mlr nicely.
• Embrace
  data.table for
  fast and convenient data frame computations.
• Combine data.table and R6, for this we will make heavy use of
  list columns in data.tables.
• Defensive programming and type safety. All user input is checked
  with checkmate.
  Return types are documented, and mechanisms popular in base R which
  “simplify” the result unpredictably (e.g., sapply() or drop
  argument in [.data.frame) are avoided.
• Be light on dependencies. mlr3 requires the following packages at
  runtime:
  • backports:
    Ensures backward compatibility with older R releases. Developed
    by members of the mlr team. No recursive dependencies.
  • checkmate:
    Fast argument checks. Developed by members of the mlr team. No
    extra recursive dependencies.
  • mlr3misc:
    Miscellaneous functions used in multiple mlr3 extension
    packages.
    Developed by the mlr team. No extra recursive dependencies.
  • paradox:
    Descriptions for parameters and parameter sets. Developed by the
    mlr team. No extra recursive dependencies.
  • R6: Reference class
    objects. No recursive dependencies.
  • data.table:
    Extension of R’s data.frame. No recursive dependencies.
  • digest: Hash
    digests. No recursive dependencies.
  • uuid: Create unique
    string identifiers. No recursive dependencies.
  • lgr: Logging
    facility. No extra recursive dependencies.
  • mlr3measures:
    Performance measures. No extra recursive dependencies.
  • mlbench: A
    collection of machine learning data sets. No dependencies.
• Reflections:
  Objects are queryable for properties and capabilities, allowing you
  to program on them.
• Additional functionality that comes with extra dependencies:
  • For parallelization, mlr3 utilizes the
    future and
    future.apply
    packages.
  • To capture output, warnings and exceptions,
    evaluate and
    callr can be used.

Extension Packages

Consult the
wiki for
short descriptions and links to the respective repositories.

Contributing to mlr3

This R package is licensed under the
LGPL-3. If you
encounter problems using this software (lack of documentation,
misleading or wrong documentation, unexpected behaviour, bugs, …) or
just want to suggest features, please open an issue in the issue
tracker. Pull requests are
welcome and will be included at the discretion of the maintainers.

Please consult the wiki for a
style guide, a
roxygen guide and
a pull request
guide.

Citing mlr3

If you use mlr3, please cite our JOSS
article:

@Article{mlr3,
  title = {{mlr3}: A modern object-oriented machine learning framework in {R}},
  author = {Michel Lang and Martin Binder and Jakob Richter and Patrick Schratz and Florian Pfisterer and Stefan Coors and Quay Au and Giuseppe Casalicchio and Lars Kotthoff and Bernd Bischl},
  journal = {Journal of Open Source Software},
  year = {2019},
  month = {dec},
  doi = {10.21105/joss.01903},
  url = {https://joss.theoj.org/papers/10.21105/joss.01903},
}