Merge pull request #1035 from alan-turing-institute/dev

For a 0.19.3 release
JuliaAI · Aug 24, 2023 · 0d58ec1 · 0d58ec1
2 parents ed416ae + 585a120
commit 0d58ec1
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diff --git a/Project.toml b/Project.toml
@@ -1,7 +1,7 @@
 name = "MLJ"
 uuid = "add582a8-e3ab-11e8-2d5e-e98b27df1bc7"
 authors = ["Anthony D. Blaom <[email protected]>"]
-version = "0.19.2"
+version = "0.19.3"
 
 [deps]
 CategoricalArrays = "324d7699-5711-5eae-9e2f-1d82baa6b597"
@@ -11,13 +11,15 @@ Distributions = "31c24e10-a181-5473-b8eb-7969acd0382f"
 LinearAlgebra = "37e2e46d-f89d-539d-b4ee-838fcccc9c8e"
 MLJBase = "a7f614a8-145f-11e9-1d2a-a57a1082229d"
 MLJEnsembles = "50ed68f4-41fd-4504-931a-ed422449fee0"
+MLJFlow = "7b7b8358-b45c-48ea-a8ef-7ca328ad328f"
 MLJIteration = "614be32b-d00c-4edb-bd02-1eb411ab5e55"
 MLJModels = "d491faf4-2d78-11e9-2867-c94bc002c0b7"
 MLJTuning = "03970b2e-30c4-11ea-3135-d1576263f10f"
 OpenML = "8b6db2d4-7670-4922-a472-f9537c81ab66"
 Pkg = "44cfe95a-1eb2-52ea-b672-e2afdf69b78f"
 ProgressMeter = "92933f4c-e287-5a05-a399-4b506db050ca"
 Random = "9a3f8284-a2c9-5f02-9a11-845980a1fd5c"
+Reexport = "189a3867-3050-52da-a836-e630ba90ab69"
 ScientificTypes = "321657f4-b219-11e9-178b-2701a2544e81"
 Statistics = "10745b16-79ce-11e8-11f9-7d13ad32a3b2"
 StatsBase = "2913bbd2-ae8a-5f71-8c99-4fb6c76f3a91"
@@ -29,11 +31,13 @@ ComputationalResources = "0.3"
 Distributions = "0.21,0.22,0.23, 0.24, 0.25"
 MLJBase = "0.21.3"
 MLJEnsembles = "0.3"
+MLJFlow = "0.1"
 MLJIteration = "0.5"
 MLJModels = "0.16"
 MLJTuning = "0.7"
 OpenML = "0.2,0.3"
 ProgressMeter = "1.1"
+Reexport = "1.2"
 ScientificTypes = "3"
 StatsBase = "0.32,0.33, 0.34"
 Tables = "0.2,1.0"

diff --git a/ROADMAP.md b/ROADMAP.md
@@ -1,27 +1,27 @@
 # Road map
 
-February 2020; updated, May 2021
+February 2020; updated, July 2023
 
 Please visit [contributing guidelines](CONTRIBUTING.md) if interested
 in contributing to MLJ.
 
-### Guiding goals
+### Goals
 
 -   **Usability, interoperability, extensibility, reproducibility,**
 	and **code transparency**.
 
 -   Offer state-of-art tools for model **composition** and model
 	**optimization** (hyper-parameter tuning)
 
--   Avoid common **pain-points** of other frameworks:
+-   Avoid common **pain-points** of other frameworks with MLJ:
 
-	-   identifying all models that solve a given task
+	-   identify and list all models that solve a given task
 
-	-   routine operations requiring a lot of code
+	-   easily perform routine operations requiring a lot of code
 
-	-   passage from data source to algorithm-specific data format
+	-   easily transform data, from source to algorithm-specific data format
 
-	-   probabilistic predictions: inconsistent representations, lack
+	-   make use of probabilistic predictions: no more inconsistent representations / lack
 		of options for performance evaluation
 
 -   Add some focus to julia machine learning software development more

diff --git a/docs/ModelDescriptors.toml b/docs/ModelDescriptors.toml
@@ -141,6 +141,7 @@ MultitargetLinearRegressor_MultivariateStats = ["regression"]
 MultitargetNeuralNetworkRegressor_BetaML = ["regression"]
 MultitargetNeuralNetworkRegressor_MLJFlux = ["regression", "iterative_models"]
 MultitargetRidgeRegressor_MultivariateStats = ["regression"]
+MultitargetSRRegressor_SymbolicRegression = ["regression"]
 NeuralNetworkClassifier_BetaML = ["classification"]
 NeuralNetworkClassifier_MLJFlux = ["classification", "iterative_models"]
 NeuralNetworkRegressor_BetaML = ["regression"]
@@ -187,6 +188,11 @@ SGDClassifier_MLJScikitLearnInterface = ["classification"]
 SGDRegressor_MLJScikitLearnInterface = ["regression"]
 SODDetector_OutlierDetectionPython = ["outlier_detection", "outlier_detection"]
 SOSDetector_OutlierDetectionPython = ["outlier_detection"]
+SRRegressor_SymbolicRegression = ["regression"]
+StableForestClassifier_SIRUS = ["classification"]
+StableForestRegressor_SIRUS = ["regression"]
+StableRulesClassifier_SIRUS = ["classification"]
+StableRulesRegressor_SIRUS = ["regression"]
 SVC_LIBSVM = ["classification"]
 SVMClassifier_MLJScikitLearnInterface = ["classification"]
 SVMLinearClassifier_MLJScikitLearnInterface = ["classification"]

diff --git a/docs/make.jl b/docs/make.jl
@@ -74,6 +74,7 @@ pages = [
     "Learning Networks" => "learning_networks.md",
     "Controlling Iterative Models" => "controlling_iterative_models.md",
     "Generating Synthetic Data" => "generating_synthetic_data.md",
+    "Logging Workflows" => "logging_workflows.md",
     "OpenML Integration" => "openml_integration.md",
     "Acceleration and Parallelism" => "acceleration_and_parallelism.md",
     "Simple User Defined Models" => "simple_user_defined_models.md",

diff --git a/docs/src/evaluating_model_performance.md b/docs/src/evaluating_model_performance.md
@@ -9,8 +9,11 @@ In addition to hold-out and cross-validation, the user can specify
 an explicit list of train/test pairs of row indices for resampling, or
 define new resampling strategies.
 
-For simultaneously evaluating *multiple* models and/or data
-sets, see [Benchmarking](benchmarking.md).
+For simultaneously evaluating *multiple* models, see [Comparing models of different type
+and nested cross-validation](@ref).
+
+For externally logging the outcomes of performance evaluation experiments, see [Logging
+Workflows](@ref)
 
 ## Evaluating against a single measure
 

diff --git a/docs/src/index.md b/docs/src/index.md
@@ -75,6 +75,10 @@ To support MLJ development, please cite these works or star the repo:
 [Model Stacking](@ref) |
 [Learning Networks](@ref)
 
+### Integration 
+[Logging Workflows](@ref) |
+[OpenML Integration](@ref)
+
 ### Customization and Extension
 [Simple User Defined Models](@ref) |
 [Quick-Start Guide to Adding Models](@ref) |

diff --git a/docs/src/list_of_supported_models.md b/docs/src/list_of_supported_models.md
@@ -50,6 +50,8 @@ independent assessment.
 [ParallelKMeans.jl](https://github.com/PyDataBlog/ParallelKMeans.jl) | - | KMeans | experimental |
 [PartialLeastSquaresRegressor.jl](https://github.com/lalvim/PartialLeastSquaresRegressor.jl) | - | PLSRegressor, KPLSRegressor | experimental |
 [ScikitLearn.jl](https://github.com/cstjean/ScikitLearn.jl) | [MLJScikitLearnInterface.jl](https://github.com/JuliaAI/MLJScikitLearnInterface.jl) | ARDRegressor, AdaBoostClassifier, AdaBoostRegressor, AffinityPropagation, AgglomerativeClustering, BaggingClassifier, BaggingRegressor, BayesianLDA, BayesianQDA, BayesianRidgeRegressor, BernoulliNBClassifier, Birch, ComplementNBClassifier, DBSCAN, DummyClassifier, DummyRegressor, ElasticNetCVRegressor, ElasticNetRegressor, ExtraTreesClassifier, ExtraTreesRegressor, FeatureAgglomeration, GaussianNBClassifier, GaussianProcessClassifier, GaussianProcessRegressor, GradientBoostingClassifier, GradientBoostingRegressor, HuberRegressor, KMeans, KNeighborsClassifier, KNeighborsRegressor, LarsCVRegressor, LarsRegressor, LassoCVRegressor, LassoLarsCVRegressor, LassoLarsICRegressor, LassoLarsRegressor, LassoRegressor, LinearRegressor, LogisticCVClassifier, LogisticClassifier, MeanShift, MiniBatchKMeans, MultiTaskElasticNetCVRegressor, MultiTaskElasticNetRegressor, MultiTaskLassoCVRegressor, MultiTaskLassoRegressor, MultinomialNBClassifier, OPTICS, OrthogonalMatchingPursuitCVRegressor, OrthogonalMatchingPursuitRegressor, PassiveAggressiveClassifier, PassiveAggressiveRegressor, PerceptronClassifier, ProbabilisticSGDClassifier, RANSACRegressor, RandomForestClassifier, RandomForestRegressor, RidgeCVClassifier, RidgeCVRegressor, RidgeClassifier, RidgeRegressor, SGDClassifier, SGDRegressor, SVMClassifier, SVMLClassifier, SVMLRegressor, SVMNuClassifier, SVMNuRegressor, SVMRegressor, SpectralClustering, TheilSenRegressor | high² |
+[SIRUS.jl](https://github.com/rikhuijzer/SIRUS.jl) | - | StableForestClassifier, StableForestRegressor, StableRulesClassifier, StableRulesRegressor | low |
+[SymbolicRegression.jl](https://github.com/MilesCranmer/SymbolicRegression.jl) | - | MultitargetSRRegressor, SRRegressor | experimental |
 [TSVD.jl](https://github.com/JuliaLinearAlgebra/TSVD.jl) | [MLJTSVDInterface.jl](https://github.com/JuliaAI/MLJTSVDInterface.jl) | TSVDTransformer | high | 
 [XGBoost.jl](https://github.com/dmlc/XGBoost.jl) | [MLJXGBoostInterface.jl](https://github.com/JuliaAI/MLJXGBoostInterface.jl) | XGBoostRegressor, XGBoostClassifier, XGBoostCount | high |
 

diff --git a/docs/src/logging_workflows.md b/docs/src/logging_workflows.md
@@ -0,0 +1,12 @@
+# Logging Workflows
+
+## MLflow integration
+
+[MLflow](https://mlflow.org) is a popular, language-agnostic, tool for externally logging
+the outcomes of machine learning experiments, including those carried out using MLJ.
+
+This functionality is provided by the [MLJFlow.jl](https://github.com/JuliaAI/MLJFlow.jl)
+package whose methods are automatically available to MLJ users. Refer to the package's
+documentation for examples.
+
+
diff --git a/src/MLJ.jl b/src/MLJ.jl
@@ -8,13 +8,16 @@ import Distributed: @distributed, nworkers, pmap
 import Pkg
 import Pkg.TOML
 
+using Reexport
+
 # from the MLJ universe:
 using MLJBase
 import MLJBase.save
 using MLJEnsembles
 using MLJTuning
 using MLJModels
 using OpenML
+@reexport using MLJFlow
 using MLJIteration
 import MLJIteration.IterationControl
 
@@ -89,7 +92,7 @@ export nrows, color_off, color_on,
     @load_boston, @load_ames, @load_iris, @load_reduced_ames, @load_crabs,
     load_boston, load_ames, load_iris, load_reduced_ames, load_crabs,
     Machine, machine, AbstractNode, @node,
-    source, node, fit!, freeze!, thaw!, Node, sources, origins,
+
     machines, sources, anonymize!, @from_network, fitresults,
     @pipeline, Stack, Pipeline, TransformedTargetModel,
     ResamplingStrategy, Holdout, CV, TimeSeriesCV,

diff --git a/test/exported_names.jl b/test/exported_names.jl
@@ -22,4 +22,9 @@ Save()
 
 @test OpenML.load isa Function
 
+
+# MLJFlow
+
+MLFlowLogger
+
 true