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README.Rmd
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README.Rmd
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---
output: github_document
---
```{r, include = FALSE}
lgr::get_logger("mlr3")$set_threshold("warn")
set.seed(1)
options(datatable.print.class = FALSE, datatable.print.keys = FALSE)
```
# mlr3 <img src="man/figures/logo.png" align="right" width = "120" />
Package website: [release](https://mlr3.mlr-org.com/) | [dev](https://mlr3.mlr-org.com/dev/)
Efficient, object-oriented programming on the building blocks of machine learning.
Successor of [mlr](https://github.com/mlr-org/mlr).
<!-- badges: start -->
[](https://github.com/mlr-org/mlr3/actions)
[](https://doi.org/10.21105/joss.01903)
[](https://cran.r-project.org/package=mlr3)
[](https://stackoverflow.com/questions/tagged/mlr3)
[](https://lmmisld-lmu-stats-slds.srv.mwn.de/mlr_invite/)
<!-- badges: end -->
## Resources (for users and developers)
* We _started_ writing a [book](https://mlr3book.mlr-org.com/).
This should be the central entry point to the package.
* The [mlr3gallery](https://mlr3gallery.mlr-org.com) has some case studies and demonstrates how frequently occurring problems can be solved.
It is still in early days so stay tuned for more to come.
* [Reference manual](https://mlr3.mlr-org.com/reference/)
* [FAQ](https://github.com/mlr-org/mlr3/wiki/FAQ)
* **Cheatsheets**
- [Overview of cheatsheets](https://cheatsheets.mlr-org.com)
- [mlr3](https://cheatsheets.mlr-org.com/mlr3.pdf)
- [mlr3tuning](https://cheatsheets.mlr-org.com/mlr3tuning.pdf)
- [mlr3pipelines](https://cheatsheets.mlr-org.com/mlr3pipelines.pdf)
* **Videos**:
- [useR2019 talk on mlr3](https://www.youtube.com/watch?v=wsP2hiFnDQs)
- [useR2019 talk on mlr3pipelines and mlr3tuning](https://www.youtube.com/watch?v=gEW5RxkbQuQ)
- [useR2020 tutorial on mlr3, mlr3tuning and mlr3pipelines](https://www.youtube.com/watch?v=T43hO2o_nZw)
* **Courses/Lectures**
- The course [Introduction to Machine learning (I2ML)](https://compstat-lmu.github.io/lecture_i2ml/) is a free and open flipped classroom course on the basics of machine learning. `mlr3` is used in the [demos](https://github.com/compstat-lmu/lecture_i2ml/tree/master/code-demos-pdf) and [exercises](https://github.com/compstat-lmu/lecture_i2ml/tree/master/exercises).
* **Templates/Tutorials**
- [mlr3-learndrake](https://github.com/mlr-org/mlr3-learndrake): Shows how to use mlr3 with [drake](https://docs.ropensci.org/drake/) for reproducible ML workflow automation.
* [List of extension packages](https://github.com/mlr-org/mlr3/wiki/Extension-Packages)
* [mlr-outreach](https://github.com/mlr-org/mlr-outreach) contains public talks and slides resources.
* Our [blog](https://mlr-org.com/) about _mlr_ and _mlr3_.
(We are not the most frequent bloggers ;) )
* [Wiki](https://github.com/mlr-org/mlr3/wiki):
Contains mainly information for developers.
## Installation
Install the last release from CRAN:
```{r eval = FALSE}
install.packages("mlr3")
```
Install the development version from GitHub:
```{r eval = FALSE}
remotes::install_github("mlr-org/mlr3")
```
If you want to get started with `mlr3`, we recommend installing the [mlr3verse](https://mlr3verse.mlr-org.com/) meta-package which installs `mlr3` and some of the most important extension packages:
```{r eval = FALSE}
install.packages("mlr3verse")
```
## Example
### Constructing Learners and Tasks
```{r}
library(mlr3)
# create learning task
task_iris <- TaskClassif$new(id = "iris", backend = iris, target = "Species")
task_iris
# load learner and set hyperparameter
learner <- lrn("classif.rpart", cp = .01)
```
### Basic train + predict
```{r}
# 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
measure <- msr("classif.acc")
prediction$score(measure)
```
### Resample
```{r}
# automatic resampling
resampling <- rsmp("cv", folds = 3L)
rr <- resample(task_iris, learner, resampling)
rr$score(measure)
rr$aggregate(measure)
```
## Extension Packages
<a href="https://raw.githubusercontent.com/mlr-org/mlr3/master/man/figures/mlr3verse.svg?sanitize=true"><img src="man/figures/mlr3verse.svg" /></a>
Consult the [wiki](https://github.com/mlr-org/mlr3/wiki/Extension-Packages) for short descriptions and links to the respective repositories.
For beginners, we strongly recommend to install and load the [mlr3verse](https://mlr3verse.mlr-org.com/) package for a better user experience.
## Why a rewrite?
[mlr](https://github.com/mlr-org/mlr) was first released to [CRAN](https://cran.r-project.org/package=mlr) in 2013.
Its core design and architecture date back even further.
The addition of many features has led to a [feature creep](https://en.wikipedia.org/wiki/Feature_creep) which makes [mlr](https://github.com/mlr-org/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](https://github.com/mlr-org/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](https://adv-r.hadley.nz/s3.html) with the help of [R6](https://cran.r-project.org/package=R6).
* Embrace [R6](https://cran.r-project.org/package=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`](https://cran.r-project.org/package=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`](https://cran.r-project.org/package=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:
- [`future.apply`](https://cran.r-project.org/package=future.apply):
Resampling and benchmarking is parallelized with the [`future`](https://cran.r-project.org/package=future) abstraction interfacing many parallel backends.
- [`backports`](https://cran.r-project.org/package=backports):
Ensures backward compatibility with older R releases. Developed by members of the `mlr` team.
No recursive dependencies.
- [`checkmate`](https://cran.r-project.org/package=checkmate):
Fast argument checks. Developed by members of the `mlr` team.
No extra recursive dependencies.
- [`mlr3misc`](https://cran.r-project.org/package=mlr3misc):
Miscellaneous functions used in multiple mlr3 [extension packages](https://github.com/mlr-org/mlr3/wiki/Extension-Packages).
Developed by the `mlr` team.
No extra recursive dependencies.
- [`paradox`](https://cran.r-project.org/package=paradox):
Descriptions for parameters and parameter sets. Developed by the `mlr` team.
No extra recursive dependencies.
- [`R6`](https://cran.r-project.org/package=R6):
Reference class objects.
No recursive dependencies.
- [`data.table`](https://cran.r-project.org/package=data.table):
Extension of R's `data.frame`.
No recursive dependencies.
- [`digest`](https://cran.r-project.org/package=digest):
Hash digests.
No recursive dependencies.
- [`uuid`](https://cran.r-project.org/package=uuid):
Create unique string identifiers.
No recursive dependencies.
- [`lgr`](https://cran.r-project.org/package=lgr):
Logging facility.
No extra recursive dependencies.
- [`mlr3measures`](https://cran.r-project.org/package=mlr3measures):
Performance measures.
No extra recursive dependencies.
- [`mlbench`](https://cran.r-project.org/package=mlbench):
A collection of machine learning data sets.
No dependencies.
* [Reflections](https://en.wikipedia.org/wiki/Reflection_%28computer_programming%29): Objects are queryable for properties and capabilities, allowing you to program on them.
* Additional functionality that comes with extra dependencies:
- To capture output, warnings and exceptions, [`evaluate`](https://cran.r-project.org/package=evaluate) and [`callr`](https://cran.r-project.org/package=callr) can be used.
## Contributing to mlr3
This R package is licensed under the [LGPL-3](https://www.gnu.org/licenses/lgpl-3.0.en.html).
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](https://github.com/mlr-org/mlr3/issues).
Pull requests are welcome and will be included at the discretion of the maintainers.
Please consult the [wiki](https://github.com/mlr-org/mlr3/wiki/) for a [style guide](https://github.com/mlr-org/mlr3/wiki/Style-Guide), a [roxygen guide](https://github.com/mlr-org/mlr3/wiki/Roxygen-Guide) and a [pull request guide](https://github.com/mlr-org/mlr3/wiki/PR-Guidelines).
## Citing mlr3
If you use mlr3, please cite our [JOSS article](https://doi.org/10.21105/joss.01903):
```{r echo = FALSE, comment = ""}
toBibtex(citation("mlr3"))
```