Merge branch 'dev' into patch-1

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)

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).

ORGANIZATION.md

@@ -8,102 +8,85 @@ 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
-  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
-  manual](src/docs) which documents functionality across the
-  repositories, with the exception of ScientificTypesBase, and
-  MLJScientific types which host their own documentation. (The MLJ
-  manual and MLJTutorials do provide overviews of scientific types.)
-
-* [MLJModelInterface.jl](https://github.com/JuliaAI/MLJModelInterface.jl)
-  is a lightweight package imported by packages implementing MLJ's
-  interface for their machine learning models. It's only dependencies
-  are ScientificTypesBase.jl (which depends only on the standard
-  library module `Random`) and
-  [StatisticalTraits.jl](https://github.com/JuliaAI/StatisticalTraits.jl)
-  (which depends only on ScientificTypesBase.jl).
+* [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
+  manual](src/docs) which documents functionality across the repositories, although some
+  pages point to documentation hosted locally by a particular package.
+
+
+* [MLJModelInterface.jl](https://github.com/JuliaAI/MLJModelInterface.jl) is a lightweight
+  package imported by packages implementing MLJ's interface for their machine learning
+  models. It's only dependencies are ScientificTypesBase.jl (which depends only on the
+  standard library module `Random`) and
+  [StatisticalTraits.jl](https://github.com/JuliaAI/StatisticalTraits.jl) (which depends
+  only on ScientificTypesBase.jl).
 
-* (⟂)
-  [MLJBase.jl](https://github.com/JuliaAI/MLJBase.jl) is
-  a large repository with two main purposes: (i) to give "dummy"
-  methods defined in MLJModelInterface their intended functionality
-  (which depends on third party packages, such as
+* (⟂) [MLJBase.jl](https://github.com/JuliaAI/MLJBase.jl) is a large repository with two
+  main purposes: (i) to give "dummy" methods defined in MLJModelInterface their intended
+  functionality (which depends on third party packages, such as
   [Tables.jl](https://github.com/JuliaData/Tables.jl),
-  [Distributions.jl](https://github.com/JuliaStats/Distributions.jl)
-  and
-  [CategoricalArrays.jl](https://github.com/JuliaData/CategoricalArrays.jl));
-  and (ii) provide functionality essential to the MLJ user that has
-  not been relegated to its own "satellite" repository for some
-  reason. See the [MLJBase.jl
-  readme](https://github.com/JuliaAI/MLJBase.jl) for a
-  detailed description of MLJBase's contents.
+  [Distributions.jl](https://github.com/JuliaStats/Distributions.jl) and
+  [CategoricalArrays.jl](https://github.com/JuliaData/CategoricalArrays.jl)); and (ii)
+  provide functionality essential to the MLJ user that has not been relegated to its own
+  "satellite" repository for some reason. See the [MLJBase.jl
+  readme](https://github.com/JuliaAI/MLJBase.jl) for a detailed description of MLJBase's
+  contents.
 
-* [StatisticalMeasures.jl](https://github.com/JuliaAI/StatisticalMeasures.jl) provifes
+* [StatisticalMeasures.jl](https://github.com/JuliaAI/StatisticalMeasures.jl) provides
   performance measures (metrics) such as losses and scores.
 
-* [MLJModels.jl](https://github.com/JuliaAI/MLJModels.jl)
-  hosts the *MLJ model registry*, which contains metadata on all the
-  models the MLJ user can search and load from MLJ. Moreover, it
-  provides the functionality for **loading model code** from MLJ on
-  demand. Finally, it furnishes some commonly used transformers for
-  data pre-processing, such as `ContinuousEncoder` and `Standardizer`.
+* [MLJModels.jl](https://github.com/JuliaAI/MLJModels.jl) hosts the *MLJ model registry*,
+  which contains metadata on all the models the MLJ user can search and load from
+  MLJ. Moreover, it provides the functionality for **loading model code** from MLJ on
+  demand. Finally, it furnishes some commonly used transformers for data pre-processing,
+  such as `ContinuousEncoder` and `Standardizer`.
 
-* [MLJTuning.jl](https://github.com/JuliaAI/MLJTuning.jl)
-  provides MLJ's `TunedModel` wrapper for hyper-parameter
-  optimization, including the extendable API for tuning strategies,
-  and selected in-house implementations, such as `Grid` and
-  `RandomSearch`.
+* [MLJTuning.jl](https://github.com/JuliaAI/MLJTuning.jl) provides MLJ's `TunedModel`
+  wrapper for hyper-parameter optimization, including the extendable API for tuning
+  strategies, and selected in-house implementations, such as `Grid` and `RandomSearch`.
 
-* [MLJEnsembles.jl](https://github.com/JuliaAI/MLJEnsembles.jl)
-  provides MLJ's `EnsembleModel` wrapper, for creating homogenous
-  model ensembles.
+* [MLJEnsembles.jl](https://github.com/JuliaAI/MLJEnsembles.jl) provides MLJ's
+  `EnsembleModel` wrapper, for creating homogeneous model ensembles.
 
-* [MLJIteration.jl](https://github.com/JuliaAI/MLJIteration.jl)
-  provides the `IteratedModel` wrapper for controlling iterative
-  models (snapshots, early stopping criteria, etc)
+* [MLJIteration.jl](https://github.com/JuliaAI/MLJIteration.jl) provides the
+  `IteratedModel` wrapper for controlling iterative models (snapshots, early stopping
+  criteria, etc)
 
-* (⟂)
-  [OpenML.jl](https://github.com/JuliaAI/OpenML.jl) provides
-  integration with the [OpenML](https://www.openml.org) data science
-  exchange platform
+* [MLJFlow.jl](https://github.com/JuliaAI/MLJFlow.jl) provides integration with the
+  platform-agnostic machine learning tracking tool [MLflow](https://mlflow.org).
 
-* (⟂)
-  [MLJLinearModels.jl](https://github.com/JuliaAI/MLJLinearModels.jl)
-  is an experimental package for a wide range of julia-native penalized linear models
-  such as Lasso, Elastic-Net, Robust regression, LAD regression,
-  etc. 
+* (⟂) [OpenML.jl](https://github.com/JuliaAI/OpenML.jl) provides integration with the
+  [OpenML](https://www.openml.org) data science exchange platform
+
+* (⟂) [MLJLinearModels.jl](https://github.com/JuliaAI/MLJLinearModels.jl) provides a wide
+  range of julia-native penalized linear models such as Lasso, Elastic-Net, Robust
+  regression, LAD regression, etc.
 
-* [MLJFlux.jl](https://github.com/FluxML/MLJFlux.jl) an experimental
-  package for gradient-descent models, such as traditional
-  neural-networks, built with
+* [MLJFlux.jl](https://github.com/FluxML/MLJFlux.jl) an experimental package for
+  gradient-descent models, such as traditional neural-networks, built with
   [Flux.jl](https://github.com/FluxML/Flux.jl), in MLJ.
 
-* (⟂)
-  [ScientificTypesBase.jl](https://github.com/JuliaAI/ScientificTypesBase.jl)
-  is an ultra lightweight package providing "scientific" types,
-  such as `Continuous`, `OrderedFactor`, `Image` and `Table`. It's
-  purpose is to formalize conventions around the scientific
-  interpretation of ordinary machine types, such as `Float32` and
+* (⟂) [ScientificTypesBase.jl](https://github.com/JuliaAI/ScientificTypesBase.jl) is an
+  ultra lightweight package providing "scientific" types, such as `Continuous`,
+  `OrderedFactor`, `Image` and `Table`. It's purpose is to formalize conventions around
+  the scientific interpretation of ordinary machine types, such as `Float32` and
   `DataFrame`.
 
-* (⟂)
-  [ScientificTypes.jl](https://github.com/JuliaAI/ScientificTypes.jl)
-  articulates the particular convention for the scientific interpretation of
-  data that MLJ adopts
+* (⟂) [ScientificTypes.jl](https://github.com/JuliaAI/ScientificTypes.jl) articulates the
+  particular convention for the scientific interpretation of data that MLJ adopts
 
-* (⟂)
-  [StatisticalTraits.jl](https://github.com/JuliaAI/StatisticalTraits.jl)
-  An ultra lightweight package defining fall-back implementations for
-  a collection of traits possessed by statistical objects, principally
-  models and measures (metrics).
+* (⟂) [StatisticalTraits.jl](https://github.com/JuliaAI/StatisticalTraits.jl) An ultra
+  lightweight package defining fall-back implementations for a collection of traits
+  possessed by statistical objects, principally models and measures (metrics).
+
+* (⟂) [DataScienceTutorials](https://github.com/JuliaAI/DataScienceTutorials.jl) collects
+  tutorials on how to use MLJ, which are deployed
+  [here](https://JuliaAI.github.io/DataScienceTutorials.jl/)
 
-* (⟂)
-  [DataScienceTutorials](https://github.com/alan-turing-institute/DataScienceTutorials.jl)
-  collects tutorials on how to use MLJ, which are deployed
-  [here](https://alan-turing-institute.github.io/DataScienceTutorials.jl/)
+* [MLJTestInterface](https://github.com/JuliaAI/MLJTestInterface.jl) provides tests for
+  implementations of the MLJ model interface
 
-* [MLJTestIntegration](https://github.com/JuliaAI/MLJTestIntegration.jl)
-  provides tests for implementations of the MLJ model interface, and
-  integration tests for the entire MLJ ecosystem
+* [MLJTestIntegration](https://github.com/JuliaAI/MLJTestIntegration.jl) provides tests
+  for the entire MLJ ecosystem. (Called when you run `ENV["MLJ_TEST_INTEGRATION"]="true";
+  Pkg.test("MLJ")`.

README.md

@@ -4,11 +4,11 @@
 
 <h2 align="center">A Machine Learning Framework for Julia
 <p align="center">
-  <a href="https://github.com/alan-turing-institute/MLJ.jl/actions">
-    <img src="https://github.com/alan-turing-institute/MLJ.jl/workflows/CI/badge.svg"
+  <a href="https://github.com/JuliaAI/MLJ.jl/actions">
+    <img src="https://github.com/JuliaAI/MLJ.jl/workflows/CI/badge.svg"
          alt="Build Status">
   </a>
-  <a href="https://alan-turing-institute.github.io/MLJ.jl/dev/">
+  <a href="https://JuliaAI.github.io/MLJ.jl/dev/">
     <img src="https://img.shields.io/badge/docs-stable-blue.svg"
          alt="Documentation">
   </a>
@@ -28,28 +28,29 @@
 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). 
 
 **PhD and Postdoc opportunies** See [here](https://sebastian.vollmer.ms/jobs/).
 
 MLJ was initially created as a Tools, Practices and Systems project at
 the [Alan Turing Institute](https://www.turing.ac.uk/)
-in 2019. Current funding is provided by a [New Zealand Strategic
+in 2019. Funding has also been provided by a [New Zealand Strategic
 Science Investment
 Fund](https://www.mbie.govt.nz/science-and-technology/science-and-innovation/funding-information-and-opportunities/investment-funds/strategic-science-investment-fund/ssif-funded-programmes/university-of-auckland/)
 awarded to the University of Auckland.
 
 MLJ has been developed with the support of the following organizations:
 
 <div align="center">
+    <img src="material/DFKI.png" width = 100/>
     <img src="material/Turing_logo.png" width = 100/>
     <img src="material/UoA_logo.png" width = 100/>
     <img src="material/IQVIA_logo.png" width = 100/>