diff --git a/CODE_OF_CONDUCT.md b/CODE_OF_CONDUCT.md index 8dad59580..22f5e88a8 100644 --- a/CODE_OF_CONDUCT.md +++ b/CODE_OF_CONDUCT.md @@ -18,7 +18,7 @@ below. The Committee will respond to reports on a case-by-case basis. following forums: - the GitHub repository - [MLJ.jl](https://github.com/alan-turing-institute/MLJ.jl) (including + [MLJ.jl](https://github.com/JuliaAI/MLJ.jl) (including all issues, discussions, and pull requests) - all GitHub repositories in the [JuliaAI](https://github.com/JuliaAI) diff --git a/CONTRIBUTING.md b/CONTRIBUTING.md index 91b08ed75..2dcf2a643 100644 --- a/CONTRIBUTING.md +++ b/CONTRIBUTING.md @@ -9,7 +9,7 @@ channel](https://julialang.org/slack/), #mlj. - [Code organization](ORGANIZATION.md) - Issues: Currently issues are split between [MLJ - issues](https://github.com/alan-turing-institute/MLJ.jl/issues) and + issues](https://github.com/JuliaAI/MLJ.jl/issues) and issues in all other repositories, collected in [this GitHub Project](https://github.com/orgs/JuliaAI/projects/1). @@ -43,7 +43,7 @@ an internal state reflecting the outcomes of applying `fit!` and A generalization of machine, called a *nodal* machine, is a key element of *learning networks* which combine several models together, and form the basis for specifying new composite model types. See -[here](https://alan-turing-institute.github.io/MLJ.jl/dev/composing_models/) +[here](https://JuliaAI.github.io/MLJ.jl/dev/composing_models/) for more on these. MLJ code is now spread over [multiple repositories](ORGANIZATION.md). diff --git a/ORGANIZATION.md b/ORGANIZATION.md index fd6ec873e..6e3d5673a 100644 --- a/ORGANIZATION.md +++ b/ORGANIZATION.md @@ -8,7 +8,7 @@ connections do not currently exist but are planned/proposed.* Repositories of some possible interest outside of MLJ, or beyond its conventional use, are marked with a ⟂ symbol: -* [MLJ.jl](https://github.com/alan-turing-institute/MLJ.jl) is the +* [MLJ.jl](https://github.com/JuliaAI/MLJ.jl) is the general user's point-of-entry for choosing, loading, composing, evaluating and tuning machine learning models. It pulls in most code from other repositories described below. MLJ also hosts the [MLJ @@ -100,9 +100,9 @@ its conventional use, are marked with a ⟂ symbol: models and measures (metrics). * (⟂) - [DataScienceTutorials](https://github.com/alan-turing-institute/DataScienceTutorials.jl) + [DataScienceTutorials](https://github.com/JuliaAI/DataScienceTutorials.jl) collects tutorials on how to use MLJ, which are deployed - [here](https://alan-turing-institute.github.io/DataScienceTutorials.jl/) + [here](https://JuliaAI.github.io/DataScienceTutorials.jl/) * [MLJTestIntegration](https://github.com/JuliaAI/MLJTestIntegration.jl) provides tests for implementations of the MLJ model interface, and diff --git a/README.md b/README.md index f06796390..fbff8b342 100644 --- a/README.md +++ b/README.md @@ -4,11 +4,11 @@

A Machine Learning Framework for Julia

- - + Build Status - + Documentation @@ -28,13 +28,13 @@ MLJ (Machine Learning in Julia) is a toolbox written in Julia providing a common interface and meta-algorithms for selecting, tuning, evaluating, composing and comparing about [200 machine learning -models](https://alan-turing-institute.github.io/MLJ.jl/dev/model_browser/#Model-Browser) +models](https://JuliaAI.github.io/MLJ.jl/dev/model_browser/#Model-Browser) written in Julia and other languages. -**New to MLJ?** Start [here](https://alan-turing-institute.github.io/MLJ.jl/dev/). +**New to MLJ?** Start [here](https://JuliaAI.github.io/MLJ.jl/dev/). **Integrating an existing machine learning model into the MLJ -framework?** Start [here](https://alan-turing-institute.github.io/MLJ.jl/dev/quick_start_guide_to_adding_models/). +framework?** Start [here](https://JuliaAI.github.io/MLJ.jl/dev/quick_start_guide_to_adding_models/). **Wanting to contribute?** Start [here](CONTRIBUTING.md). diff --git a/ROADMAP.md b/ROADMAP.md index 860cd30ee..c3ba73fa3 100644 --- a/ROADMAP.md +++ b/ROADMAP.md @@ -45,7 +45,7 @@ GH Project](https://github.com/orgs/JuliaAI/projects/1). - [ ] **Integrate deep learning** using [Flux.jl](https://github.com/FluxML/Flux.jl.git) deep learning. [Done](https://github.com/FluxML/MLJFlux.jl) but can improve the experience by: - - [x] finishing iterative model wrapper [#139](https://github.com/alan-turing-institute/MLJ.jl/issues/139) + - [x] finishing iterative model wrapper [#139](https://github.com/JuliaAI/MLJ.jl/issues/139) - [ ] improving performance by implementing data front-end after (see [MLJBase #501](https://github.com/JuliaAI/MLJBase.jl/pull/501)) but see also [this relevant discussion](https://github.com/FluxML/MLJFlux.jl/issues/97). @@ -55,7 +55,7 @@ GH Project](https://github.com/orgs/JuliaAI/projects/1). [Turing.jl](https://github.com/TuringLang/Turing.jl), [Gen](https://github.com/probcomp/Gen), [Soss.jl](https://github.com/cscherrer/Soss.jl.git) - [#157](https://github.com/alan-turing-institute/MLJ.jl/issues/157) + [#157](https://github.com/JuliaAI/MLJ.jl/issues/157) [discourse thread](https://discourse.julialang.org/t/ppl-connection-to-mlj-jl/28736) [done](https://github.com/tlienart/SossMLJ.jl) but experimental and @@ -66,12 +66,12 @@ GH Project](https://github.com/orgs/JuliaAI/projects/1). - [ ] Feature engineering (python featuretools?, recursive feature elimination ✓ done in FeatureSelection.jl :) - [#426](https://github.com/alan-turing-institute/MLJ.jl/issues/426) [MLJModels #314](https://github.com/JuliaAI/MLJModels.jl/issues/314) + [#426](https://github.com/JuliaAI/MLJ.jl/issues/426) [MLJModels #314](https://github.com/JuliaAI/MLJModels.jl/issues/314) ### Enhancing core functionality -- [x] Iterative model control [#139](https://github.com/alan-turing-institute/MLJ.jl/issues/139). [Done](https://github.com/JuliaAI/MLJIteration.jl) +- [x] Iterative model control [#139](https://github.com/JuliaAI/MLJ.jl/issues/139). [Done](https://github.com/JuliaAI/MLJIteration.jl) - [ ] **†** Add more tuning strategies. See [here](https://github.com/JuliaAI/MLJTuning.jl#what-is-provided-here) @@ -79,7 +79,7 @@ GH Project](https://github.com/orgs/JuliaAI/projects/1). wish-list. Particular focus on: - [x] random search - ([#37](https://github.com/alan-turing-institute/MLJ.jl/issues/37)) + ([#37](https://github.com/JuliaAI/MLJ.jl/issues/37)) (done) - [x] Latin hypercube @@ -88,16 +88,16 @@ GH Project](https://github.com/orgs/JuliaAI/projects/1). - [ ] Bayesian methods, starting with Gaussian Process methods a la PyMC3. Some preliminary research done. - - [ ] POC for AD-powered gradient descent [#74](https://github.com/alan-turing-institute/MLJ.jl/issues/74) + - [ ] POC for AD-powered gradient descent [#74](https://github.com/JuliaAI/MLJ.jl/issues/74) - [ ] Tuning with adaptive resource allocation, as in Hyperband. This might be implemented elegantly with the help of the recent `IterativeModel` wrapper, which applies, in particular to `TunedModel` instances [see - here](https://alan-turing-institute.github.io/MLJ.jl/dev/controlling_iterative_models/#Using-training-losses,-and-controlling-model-tuning). + here](https://JuliaAI.github.io/MLJ.jl/dev/controlling_iterative_models/#Using-training-losses,-and-controlling-model-tuning). - [ ] Genetic algorithms -[#38](https://github.com/alan-turing-institute/MLJ.jl/issues/38) +[#38](https://github.com/JuliaAI/MLJ.jl/issues/38) - [ ] Particle Swarm Optimization (current WIP, GSoC project @lhnguyen-vn) @@ -105,20 +105,20 @@ GH Project](https://github.com/orgs/JuliaAI/projects/1). #18](https://github.com/JuliaAI/MLJTuning.jl/issues/18), architecture search, and other AutoML workflows - [ ] Systematic benchmarking, probably modeled on - [MLaut](https://arxiv.org/abs/1901.03678) [#69](https://github.com/alan-turing-institute/MLJ.jl/issues/74) + [MLaut](https://arxiv.org/abs/1901.03678) [#69](https://github.com/JuliaAI/MLJ.jl/issues/74) - [ ] Give `EnsembleModel` a more extendible API and extend beyond bagging (boosting, etc) and migrate to a separate repository? - [#363](https://github.com/alan-turing-institute/MLJ.jl/issues/363) + [#363](https://github.com/JuliaAI/MLJ.jl/issues/363) - [ ] **†** Enhance complex model composition: - [x] Introduce a canned - stacking model wrapper ([POC](https://alan-turing-institute.github.io/DataScienceTutorials.jl/getting-started/stacking/)). WIP @olivierlabayle + stacking model wrapper ([POC](https://JuliaAI.github.io/DataScienceTutorials.jl/getting-started/stacking/)). WIP @olivierlabayle - [ ] Get rid of macros for creating pipelines and possibly implement target transforms as wrappers ([MLJBase - #594](https://github.com/alan-turing-institute/MLJ.jl/issues/594)) + #594](https://github.com/JuliaAI/MLJ.jl/issues/594)) WIP @CameronBieganek and @ablaom @@ -147,11 +147,11 @@ GH Project](https://github.com/orgs/JuliaAI/projects/1). could use [NaiveBayes.jl](https://github.com/dfdx/NaiveBayes.jl) as a POC (currently wrapped only for dense input) but the API needs to be finalized first - {#731](https://github.com/alan-turing-institute/MLJ.jl/issues/731). Probably + {#731](https://github.com/JuliaAI/MLJ.jl/issues/731). Probably need a new SparseTables.jl package. - [x] POC for implementation of time series models classification - [#303](https://github.com/alan-turing-institute/MLJ.jl/issues/303), + [#303](https://github.com/JuliaAI/MLJ.jl/issues/303), [ScientificTypesBase #14](https://github.com/JuliaAI/ScientificTypesBase.jl/issues/14) POC is [here](https://github.com/JuliaAI/TimeSeriesClassification.jl) - [ ] POC for time series forecasting, along lines of sktime; probably needs [MLJBase @@ -162,16 +162,16 @@ GH Project](https://github.com/orgs/JuliaAI/projects/1). - [ ] Add tools or a separate repository for visualization in MLJ. - [x] Extend visualization of tuning plots beyond two-parameters - [#85](https://github.com/alan-turing-institute/MLJ.jl/issues/85) + [#85](https://github.com/JuliaAI/MLJ.jl/issues/85) (closed). - [#416](https://github.com/alan-turing-institute/MLJ.jl/issues/416) + [#416](https://github.com/JuliaAI/MLJ.jl/issues/416) [Done](https://github.com/JuliaAI/MLJTuning.jl/pull/121) but might be worth adding alternatives suggested in issue. - - [ ] visualizing decision boundaries? [#342](https://github.com/alan-turing-institute/MLJ.jl/issues/342) + - [ ] visualizing decision boundaries? [#342](https://github.com/JuliaAI/MLJ.jl/issues/342) - [ ] provide visualizations that MLR3 provides via [mlr3viz](https://github.com/mlr-org/mlr3viz) -- [ ] Extend API to accommodate outlier detection, as provided by [OutlierDetection.jl](https://github.com/davnn/OutlierDetection.jl) [#780](https://github.com/alan-turing-institute/MLJ.jl/issues/780) WIP @davn and @ablaom +- [ ] Extend API to accommodate outlier detection, as provided by [OutlierDetection.jl](https://github.com/davnn/OutlierDetection.jl) [#780](https://github.com/JuliaAI/MLJ.jl/issues/780) WIP @davn and @ablaom - [ ] Add more pre-processing tools: @@ -194,14 +194,14 @@ GH Project](https://github.com/orgs/JuliaAI/projects/1). is merged. - [ ] Online learning support and distributed data - [#60](https://github.com/alan-turing-institute/MLJ.jl/issues/60) + [#60](https://github.com/JuliaAI/MLJ.jl/issues/60) - [ ] DAG scheduling for learning network training - [#72](https://github.com/alan-turing-institute/MLJ.jl/issues/72) + [#72](https://github.com/JuliaAI/MLJ.jl/issues/72) (multithreading first?) - [ ] Automated estimates of cpu/memory requirements - [#71](https://github.com/alan-turing-institute/MLJ.jl/issues/71) + [#71](https://github.com/JuliaAI/MLJ.jl/issues/71) - [x] Add multithreading to tuning [MLJTuning #15](https://github.com/JuliaAI/MLJTuning.jl/issues/15) diff --git a/docs/src/about_mlj.md b/docs/src/about_mlj.md index a33daba26..8adb2aa66 100755 --- a/docs/src/about_mlj.md +++ b/docs/src/about_mlj.md @@ -15,7 +15,7 @@ MLJ is released under the MIT license. A self-contained notebook and julia script of this demonstration is also available -[here](https://github.com/alan-turing-institute/MLJ.jl/tree/dev/examples/lightning_tour). +[here](https://github.com/JuliaAI/MLJ.jl/tree/dev/examples/lightning_tour). The first code snippet below creates a new Julia environment `MLJ_tour` and installs just those packages needed for the tour. See @@ -210,13 +210,13 @@ and to report issues. For a query to have maximum exposure to maintainers and users, start a discussion thread at [Julia Discourse Machine -Learning](https://github.com/alan-turing-institute/MLJ.jl) and tag +Learning](https://github.com/JuliaAI/MLJ.jl) and tag your issue "mlj". Queries can also be posted as -[issues](https://github.com/alan-turing-institute/MLJ.jl/issues), or +[issues](https://github.com/JuliaAI/MLJ.jl/issues), or on the `#mlj` slack workspace in the Julia Slack channel. Bugs, suggestions, and feature requests can be posted -[here](https://github.com/alan-turing-institute/MLJ.jl/issues). +[here](https://github.com/JuliaAI/MLJ.jl/issues). Users are also welcome to join the `#mlj` Julia slack channel to ask questions and make suggestions. @@ -269,7 +269,7 @@ packages such as DecisionTree.jl, ScikitLearn.jl or XGBoost.jl. MLJ is supported by several satellite packages (MLJTuning, MLJModelInterface, etc) which the general user is *not* required to install directly. Developers can learn more about these -[here](https://github.com/alan-turing-institute/MLJ.jl/blob/master/ORGANIZATION.md). +[here](https://github.com/JuliaAI/MLJ.jl/blob/master/ORGANIZATION.md). See also the alternative installation instructions for [Modifying Behavior](@ref). diff --git a/docs/src/benchmarking.md b/docs/src/benchmarking.md index 2294cbea0..d8d1fe3b5 100644 --- a/docs/src/benchmarking.md +++ b/docs/src/benchmarking.md @@ -2,4 +2,4 @@ This feature not yet available. -[CONTRIBUTE.md](https://github.com/alan-turing-institute/MLJ.jl/blob/master/CONTRIBUTE.md) +[CONTRIBUTE.md](https://github.com/JuliaAI/MLJ.jl/blob/master/CONTRIBUTE.md) diff --git a/docs/src/frequently_asked_questions.md b/docs/src/frequently_asked_questions.md index c300ba01b..ae1849607 100755 --- a/docs/src/frequently_asked_questions.md +++ b/docs/src/frequently_asked_questions.md @@ -49,7 +49,7 @@ term: - **Clean probabilistic API.** The scikit-learn API does not specify a universal standard for the form of probabilistic predictions. By fixing a probabilistic API along the lines of the - [skpro](https://github.com/alan-turing-institute/skpro) project, MLJ + [skpro](https://github.com/JuliaAI/skpro) project, MLJ aims to improve support for Bayesian statistics and probabilistic graphical models. diff --git a/docs/src/index.md b/docs/src/index.md index 2c5c8e070..07b22042f 100644 --- a/docs/src/index.md +++ b/docs/src/index.md @@ -4,12 +4,12 @@

About  |  - Install  |  Learn  |  Cheatsheet  |  Workflows  |  - For Developers  |  + For Developers  |  3rd Party Packages
@@ -26,11 +26,11 @@ To support MLJ development, please cite these works or star the repo: ```@raw html + aria-label="Star JuliaAI/MLJ.jl on GitHub"> Star ``` diff --git a/docs/src/learning_mlj.md b/docs/src/learning_mlj.md index 799689d15..dce721b96 100644 --- a/docs/src/learning_mlj.md +++ b/docs/src/learning_mlj.md @@ -38,7 +38,7 @@ intended as a complete reference. - [Analyzing the Glass Dataset](https://towardsdatascience.com/part-i-analyzing-the-glass-dataset-c556788a496f): A gentle introduction to data science using Julia and MLJ (three-part blog post) -- [Lightning Tour](https://github.com/alan-turing-institute/MLJ.jl/blob/dev/examples/lightning_tour/lightning_tour.ipynb): A compressed demonstration of key MLJ functionality +- [Lightning Tour](https://github.com/JuliaAI/MLJ.jl/blob/dev/examples/lightning_tour/lightning_tour.ipynb): A compressed demonstration of key MLJ functionality - [MLJ JuliaCon2020 Workshop](https://github.com/ablaom/MachineLearningInJulia2020): older version of [MLJTutorial](https://github.com/ablaom/MLJTutorial.jl) with [video](https://www.youtube.com/watch?time_continue=27&v=qSWbCn170HU&feature=emb_title) diff --git a/docs/src/list_of_supported_models.md b/docs/src/list_of_supported_models.md index 351ed0105..7fa4ea1d7 100644 --- a/docs/src/list_of_supported_models.md +++ b/docs/src/list_of_supported_models.md @@ -5,7 +5,7 @@ the [Model Browser](@ref). MLJ provides access to a wide variety of machine learning models. We are always looking for -[help](https://github.com/alan-turing-institute/MLJ.jl/blob/master/CONTRIBUTING.md) +[help](https://github.com/JuliaAI/MLJ.jl/blob/master/CONTRIBUTING.md) adding new models or testing existing ones. Currently available models are listed below; for the most up-to-date list, run `using MLJ; models()`. diff --git a/docs/src/preparing_data.md b/docs/src/preparing_data.md index e87260707..f92bd0af3 100644 --- a/docs/src/preparing_data.md +++ b/docs/src/preparing_data.md @@ -116,7 +116,7 @@ Workshop](https://github.com/ablaom/MachineLearningInJulia2020) (specifically, [here](https://github.com/ablaom/MachineLearningInJulia2020/blob/master/tutorials.md#fixing-scientific-types-in-tabular-data)) and [this Data Science in Julia -tutorial](https://alan-turing-institute.github.io/DataScienceTutorials.jl/data/scitype/). +tutorial](https://JuliaAI.github.io/DataScienceTutorials.jl/data/scitype/). Also relevant is the section, [Working with Categorical Data](@ref). @@ -136,5 +136,5 @@ MissingImputator = @load MissingImputator pkg=BetaML [This MLJ Workshop](https://github.com/ablaom/MachineLearningInJulia2020), and the "End-to-end examples" in [Data Science in Julia -tutorials](https://alan-turing-institute.github.io/DataScienceTutorials.jl/) +tutorials](https://JuliaAI.github.io/DataScienceTutorials.jl/) give further illustrations of data preprocessing in MLJ. diff --git a/docs/src/third_party_packages.md b/docs/src/third_party_packages.md index a9a351bfe..a6ac3bd04 100644 --- a/docs/src/third_party_packages.md +++ b/docs/src/third_party_packages.md @@ -6,7 +6,7 @@ Last updated December 2020. Pull requests to update this list are very welcome. Otherwise, you may post an issue requesting this -[here](https://github.com/alan-turing-institute/MLJ.jl/issues). +[here](https://github.com/JuliaAI/MLJ.jl/issues). ## Packages providing models in the MLJ model registry @@ -16,7 +16,7 @@ See [List of Supported Models](@ref model_list) ## Providing unregistered models: - [SossMLJ.jl](https://github.com/cscherrer/SossMLJ.jl) -- [TimeSeriesClassification](https://github.com/alan-turing-institute/TimeSeriesClassification.jl) +- [TimeSeriesClassification](https://github.com/JuliaAI/TimeSeriesClassification.jl) ## Packages providing other kinds of functionality: diff --git a/examples/lightning_tour/lightning_tour.md b/examples/lightning_tour/lightning_tour.md index 8d9c99c12..3875e94f8 100644 --- a/examples/lightning_tour/lightning_tour.md +++ b/examples/lightning_tour/lightning_tour.md @@ -5,7 +5,7 @@ EditURL = "/../../../../MLJ/examples/lightning_tour/lightning_tour.jl" # Lightning tour of MLJ *For a more elementary introduction to MLJ, see [Getting -Started](https://alan-turing-institute.github.io/MLJ.jl/dev/getting_started/).* +Started](https://JuliaAI.github.io/MLJ.jl/dev/getting_started/).* **Note.** Be sure this file has not been separated from the accompanying Project.toml and Manifest.toml files, which should not