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Release 0.14.1 (#874)
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3 changes: 1 addition & 2 deletions DESCRIPTION
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Package: mlr3
Title: Machine Learning in R - Next Generation
Version: 0.14.0-9000
Version: 0.14.1
Authors@R:
c(
person("Michel", "Lang", , "michellang@gmail.com", role = c("cre", "aut"),
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Encoding: UTF-8
Config/testthat/edition: 3
Config/testthat/parallel: false
Additional_repositories: https://raphaels1.r-universe.dev/
NeedsCompilation: no
Roxygen: list(markdown = TRUE, r6 = TRUE)
RoxygenNote: 7.2.1
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9 changes: 6 additions & 3 deletions NEWS.md
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* Removed depdency on package `distr6`.
* Fixed reassembling of `GraphLearner`.
* Test set is available to the `Learner` for early stopping.
* Fixed bug that elapsed time measures were 0: https://stackoverflow.com/questions/73797845/mlr3-benchmarking-with-elapsed-time-measure
* Fixed bug where the measured elapsed time was 0:
https://stackoverflow.com/questions/73797845/mlr3-benchmarking-with-elapsed-time-measure
* Fixed `as_prediction_classif()` for `data.frame()` input (#872).
* Improved the error message when predict type of fallback learner does not
match the predict type of the learner (https://github.com/mlr-org/mlr3extralearners/issues/241)
match the predict type of the learner (mlr-org/mlr3extralearners#241).
* The test set is now available to the `Learner` during train for early
stopping.

# mlr3 0.14.0

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2 changes: 1 addition & 1 deletion README.Rmd
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Expand Up @@ -48,7 +48,7 @@ Successor of [mlr](https://github.com/mlr-org/mlr).
- [useR2020 tutorial on mlr3, mlr3tuning and mlr3pipelines](https://www.youtube.com/watch?v=T43hO2o_nZw)
- [Recorded talk about mlr3spatiotempcv and mlr3spatial at OpenDataScience Europe Conference 2021 in Wageningen, NL](https://av.tib.eu/media/55271)
* **Courses/Lectures**
- The course [Introduction to Machine learning (I2ML)](https://introduction-to-machine-learning.netlify.app/) is a free and open flipped classroom course on the basics of machine learning. `mlr3` is used in the [demos](https://github.com/slds-lmu/lecture_i2ml/tree/master/code-demos-pdf) and [exercises](https://github.com/slds-lmu/lecture_i2ml/tree/master/exercises).
- The course [Introduction to Machine learning (I2ML)](https://slds-lmu.github.io/i2ml/) is a free and open flipped classroom course on the basics of machine learning. `mlr3` is used in the [demos](https://github.com/slds-lmu/lecture_i2ml/tree/master/code-demos-pdf) and [exercises](https://github.com/slds-lmu/lecture_i2ml/tree/master/exercises).
* **Templates/Tutorials**
- [mlr3-targets](https://github.com/mlr-org/mlr3-targets): Tutorial showcasing how to use {mlr3} with [targets](https://docs.ropensci.org/targets/) for reproducible ML workflow automation.
* [List of extension packages](https://github.com/mlr-org/mlr3/wiki/Extension-Packages)
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243 changes: 120 additions & 123 deletions README.md
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Expand Up @@ -19,63 +19,61 @@ Status](https://www.r-pkg.org/badges/version-ago/mlr3)](https://cran.r-project.o

## 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)
- Ask questions on [Stackoverflow (tag
\#mlr3)](https://stackoverflow.com/questions/tagged/mlr3)
- **Extension Learners**
- Recommended core regression, classification, and survival
learners are in
[mlr3learners](https://github.com/mlr-org/mlr3learners)
- All others are in
[mlr3extralearners](https://github.com/mlr-org/mlr3extralearners)
- Use the [learner
search](https://mlr3extralearners.mlr-org.com/articles/learners/list_learners.html)
to get a simple overview
- Use the [learner
status](https://mlr3extralearners.mlr-org.com/articles/learners/test_overview.html)
to see their build status
- **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)
- [Recorded talk about mlr3spatiotempcv and mlr3spatial at
OpenDataScience Europe Conference 2021 in Wageningen,
NL](https://av.tib.eu/media/55271)
- **Courses/Lectures**
- The course [Introduction to Machine learning
(I2ML)](https://introduction-to-machine-learning.netlify.app/)
is a free and open flipped classroom course on the basics of
machine learning. `mlr3` is used in the
[demos](https://github.com/slds-lmu/lecture_i2ml/tree/master/code-demos-pdf)
and
[exercises](https://github.com/slds-lmu/lecture_i2ml/tree/master/exercises).
- **Templates/Tutorials**
- [mlr3-targets](https://github.com/mlr-org/mlr3-targets):
Tutorial showcasing how to use {mlr3} with
[targets](https://docs.ropensci.org/targets/) 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.
- 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)
- Ask questions on [Stackoverflow (tag
\#mlr3)](https://stackoverflow.com/questions/tagged/mlr3)
- **Extension Learners**
- Recommended core regression, classification, and survival learners
are in [mlr3learners](https://github.com/mlr-org/mlr3learners)
- All others are in
[mlr3extralearners](https://github.com/mlr-org/mlr3extralearners)
- Use the [learner
search](https://mlr3extralearners.mlr-org.com/articles/learners/list_learners.html)
to get a simple overview
- Use the [learner
status](https://mlr3extralearners.mlr-org.com/articles/learners/test_overview.html)
to see their build status
- **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)
- [Recorded talk about mlr3spatiotempcv and mlr3spatial at
OpenDataScience Europe Conference 2021 in Wageningen,
NL](https://av.tib.eu/media/55271)
- **Courses/Lectures**
- The course [Introduction to Machine learning
(I2ML)](https://slds-lmu.github.io/i2ml/) is a free and open flipped
classroom course on the basics of machine learning. `mlr3` is used
in the
[demos](https://github.com/slds-lmu/lecture_i2ml/tree/master/code-demos-pdf)
and
[exercises](https://github.com/slds-lmu/lecture_i2ml/tree/master/exercises).
- **Templates/Tutorials**
- [mlr3-targets](https://github.com/mlr-org/mlr3-targets): Tutorial
showcasing how to use {mlr3} with
[targets](https://docs.ropensci.org/targets/) 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

Expand Down Expand Up @@ -207,72 +205,71 @@ 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:
- [`parallelly`](https://cran.r-project.org/package=parallelly):
Helper functions for parallelization. No extra recursive
dependencies.
- [`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.
- [`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) (via
`mlr3misc`): 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.
- [`palmerpenguins`](https://cran.r-project.org/package=palmerpenguins):
A classification data set about penguins, used on examples and
provided as a toy task. 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.
- 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:
- [`parallelly`](https://cran.r-project.org/package=parallelly):
Helper functions for parallelization. No extra recursive
dependencies.
- [`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.
- [`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) (via
`mlr3misc`): 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.
- [`palmerpenguins`](https://cran.r-project.org/package=palmerpenguins):
A classification data set about penguins, used on examples and
provided as a toy task. 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

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3 changes: 2 additions & 1 deletion tests/testthat/test_Measure.R
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Expand Up @@ -125,7 +125,8 @@ test_that("time_train works with different predict type (#832)", {
})

test_that("time_train is > 0", {
skip_on_cran()
rr = resample(tsk("iris"), lrn("classif.debug"), rsmp("holdout"))
res = rr$score(msr("time_train"))
expect_true(res$time_train > 0)
expect_gt(res$time_train, 0)
})

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