Merge pull request #926 from alan-turing-institute/dev

For a 0.18.1 release

Project.toml

@@ -1,7 +1,7 @@
 name = "MLJ"
 uuid = "add582a8-e3ab-11e8-2d5e-e98b27df1bc7"
 authors = ["Anthony D. Blaom <anthony.blaom@gmail.com>"]
-version = "0.18.0"
+version = "0.18.1"
 
 [deps]
 CategoricalArrays = "324d7699-5711-5eae-9e2f-1d82baa6b597"
@@ -30,7 +30,7 @@ Distributions = "0.21,0.22,0.23, 0.24, 0.25"
 MLJBase = "0.20"
 MLJEnsembles = "0.3"
 MLJIteration = "0.5"
-MLJModels = "0.15"
+MLJModels = "0.15.5"
 MLJTuning = "0.7"
 OpenML = "0.2"
 ProgressMeter = "1.1"

docs/src/adding_models_for_general_use.md

@@ -1089,7 +1089,7 @@ MMI.save(model::SomeModel, fitresult; kwargs...) -> serializable_fitresult
 
 Implement this method to return a persistent serializable
 representation of the `fitresult` component of the `MMI.fit` return
-value.below).
+value.
 
 The fallback of `save` performs no action and returns `fitresult`.
 
@@ -1110,7 +1110,7 @@ The fallback of `restore` performs no action and returns `serializable_fitresult
 #### Example
 
 For an example, refer to the model implementations at
-[MLJXGBoostInterface.jl](https://github.com/JuliaAI/MLJXGBoostInterface.jl/blob/master/src/MLJXGBoostInterface.jl)
+[MLJXGBoostInterface.jl](https://github.com/JuliaAI/MLJXGBoostInterface.jl/blob/42afbd2974bd3bd734994004e367c98964ed1262/src/MLJXGBoostInterface.jl#L679).
 
 
 ### Document strings

docs/src/common_mlj_workflows.md

@@ -143,7 +143,7 @@ info("RidgeRegressor", pkg="MultivariateStats") # a model type in multiple packa
 
 Extracting the model document string:
 
-```@example```
+```@example workflows
 doc("DecisionTreeClassifier", pkg="DecisionTree")
 ```
 

docs/src/controlling_iterative_models.md

@@ -116,7 +116,7 @@ control                                                        | description
 [`WithReportDo`](@ref MLJIteration.WithReportDo)`(f->e->@info("report: $e))`| Call `f(r)` where `r` is the training machine report                    | yes
 [`WithModelDo`](@ref MLJIteration.WithModelDo)`(f->m->@info("model: $m))`| Call `f(m)` where `m` is the model, which may be mutated by `f`             | yes
 [`WithMachineDo`](@ref MLJIteration.WithMachineDo)`(f->mach->@info("report: $mach))`| Call `f(mach)` wher `mach` is the training machine in its current state    | yes
-[`Save`](@ref MLJIteration.Save)`(filename="machine.jlso")`|Save current training machine to `machine1.jlso`, `machine2.jslo`, etc                         | yes
+[`Save`](@ref MLJIteration.Save)`(filename="machine.jls")`|Save current training machine to `machine1.jls`, `machine2.jsl`, etc                         | yes
 
 > Table 1. Atomic controls. Some advanced options omitted.
 

docs/src/getting_started.md

@@ -3,6 +3,10 @@
 For an outline of MLJ's **goals** and **features**, see
 [About MLJ](@ref).
 
+This page introduces some MLJ basics, assuming some familiarity with
+machine learning. For a complete list of other MLJ learning resources,
+see [Learning MLJ](@ref).
+
 This section introduces only the most basic MLJ operations and
 concepts. It assumes MJL has been successfully installed. See
 [Installation](@ref) if this is not the case. 

docs/src/learning_mlj.md

@@ -7,7 +7,7 @@ See also [Getting help and reporting problems](@ref).
 The present document, although littered with examples, is primarily
 intended as a complete reference. 
 
-### Where to start?
+## Where to start?
 
 ### Completely new to Julia? 
 [Julia's learning resources page](https://julialang.org/learning/) |
@@ -28,17 +28,15 @@ intended as a complete reference.
 [MLJTutorial](https://github.com/ablaom/MLJTutorial.jl)
 
 
-### Other resources
+## Other resources
 
-- [Data Science
-Tutorials](https://juliaai.github.io/DataScienceTutorials.jl): MLJ tutorials including end-to-end examples, and "Introduction to Statistical Learning" labs
+- [Data Science Tutorials](https://juliaai.github.io/DataScienceTutorials.jl): MLJ tutorials including end-to-end examples, and "Introduction to Statistical Learning" labs
 
 - [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
 
-- [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)
+- [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)
 
 - [Learning Networks](@ref): For advanced MLJ users wanting to wrap workflows more complicated than linear pipelines
 

docs/src/mlj_cheatsheet.md

@@ -164,7 +164,7 @@ pkg="MultivariateStats")` gets all properties (aka traits) of registered models
 
 ## Saving and retrieving machines using Julia serializer
 
-`MLJ.save("trained_for_five_days.jls", mach)` to save machine `mach`
+`MLJ.save("trained_for_five_days.jls", mach)` to save machine `mach` (without data)
 
 `predict_only_mach = machine("trained_for_five_days.jlso")` to deserialize.
 

src/MLJ.jl

@@ -125,7 +125,7 @@ export Grid, RandomSearch, Explicit, TunedModel, LatinHypercube,
     learning_curve!, learning_curve
 
 # re-export from MLJModels:
-export models, localmodels, @load, @iload, load, info,
+export models, localmodels, @load, @iload, load, info, doc,
     ConstantRegressor, ConstantClassifier,     # builtins/Constant.jl
     FeatureSelector, UnivariateStandardizer,   # builtins/Transformers.jl
     Standardizer, UnivariateBoxCoxTransformer,