Merge pull request #1073 from alan-turing-institute/dev

For a 0.20.2 release

Author: ablaom

Project.toml

@@ -1,7 +1,7 @@
 name = "MLJ"
 uuid = "add582a8-e3ab-11e8-2d5e-e98b27df1bc7"
 authors = ["Anthony D. Blaom <[email protected]>"]
-version = "0.20.1"
+version = "0.20.2"
 
 [deps]
 CategoricalArrays = "324d7699-5711-5eae-9e2f-1d82baa6b597"
@@ -31,10 +31,10 @@ Tables = "bd369af6-aec1-5ad0-b16a-f7cc5008161c"
 CategoricalArrays = "0.8,0.9, 0.10"
 ComputationalResources = "0.3"
 Distributions = "0.21,0.22,0.23, 0.24, 0.25"
-MLJBase = "1"
 MLJBalancing = "0.1"
+MLJBase = "1"
 MLJEnsembles = "0.4"
-MLJFlow = "0.2"
+MLJFlow = "0.3"
 MLJIteration = "0.6"
 MLJModels = "0.16"
 MLJTuning = "0.8"
@@ -43,6 +43,7 @@ ProgressMeter = "1.1"
 Reexport = "1.2"
 ScientificTypes = "3"
 StatisticalMeasures = "0.1"
+Statistics = "1"
 StatsBase = "0.32,0.33, 0.34"
 Tables = "0.2,1.0"
 julia = "1.6"

docs/src/adding_models_for_general_use.md

@@ -1209,35 +1209,39 @@ Your document string must include the following components, in order:
 Unsupervised models implement the MLJ model interface in a very
 similar fashion. The main differences are:
 
-- The `fit` method has only one training argument `X`, as in `MLJModelInterface.fit(model,
-  verbosity, X)`. However, it has the same return value `(fitresult, cache, report)`. An
-  `update` method (e.g., for iterative models) can be optionally implemented in the same
-  way. For models that subtype `Static <: Unsupervised` (see also [Static
-  transformers](@ref) `fit` has no training arguments but does not need to be implemented
-  as a fallback returns `(nothing, nothing, nothing)`.
-
-- A `transform` method is compulsory and has the same signature as
-  `predict`, as in `MLJModelInterface.transform(model, fitresult, Xnew)`.
+- The `fit` method, which still returns `(fitresult, cache, report)` will typically have
+  only one training argument `X`, as in `MLJModelInterface.fit(model, verbosity, X)`,
+  although this is not a hard requirement. For example, a feature selection tool (wrapping
+  some supervised model) might also include a target `y` as input. Furthermore, in the
+  case of models that subtype `Static <: Unsupervised` (see also [Static
+  transformers](@ref) `fit` has no training arguments at all, but does not need to be
+  implemented as a fallback returns `(nothing, nothing, nothing)`.
+
+- A `transform` and/or `predict` method is implemented, and has the same signature as
+  `predict` does in the supervised case, as in `MLJModelInterface.transform(model,
+  fitresult, Xnew)`. However, it may only have one data argument `Xnew`, unless `model <:
+  Static`, in which case there is no restriction.  A use-case for `predict` is K-means 
+  `MLJModelInterface.predict(model, fitresult, Xnew)`. A use-case is
+  clustering that `predict`s labels and `transform`s 
+  input features into a space of lower dimension. See [Transformers
+  that also predict](@ref) for an example.
 
-- Instead of defining the `target_scitype` trait, one declares an
-  `output_scitype` trait (see above for the meaning).
+- The `target_scitype` trait continues to refer to the output of `predict`, if
+  implemented, while a trait, `output_scitype`, is for the output of `transform`.
 
 - An `inverse_transform` can be optionally implemented. The signature
   is the same as `transform`, as in
   `MLJModelInterface.inverse_transform(model, fitresult, Xout)`, which:
-
    - must make sense for any `Xout` for which `scitype(Xout) <:
-	 output_scitype(SomeSupervisedModel)` (see below); and
-
+     output_scitype(SomeSupervisedModel)` (see below); and
    - must return an object `Xin` satisfying `scitype(Xin) <:
-	 input_scitype(SomeSupervisedModel)`.
+     input_scitype(SomeSupervisedModel)`.
+
+For sample implementatations, see MLJ's [built-in
+transformers](https://github.com/JuliaAI/MLJModels.jl/blob/dev/src/builtins/Transformers.jl)
+and the clustering models at
+[MLJClusteringInterface.jl](https://github.com/jbrea/MLJClusteringInterface.jl).
 
-- A `predict` method may be optionally implemented, and has the same
-  signature as for supervised models, as in
-  `MLJModelInterface.predict(model, fitresult, Xnew)`. A use-case is
-  clustering algorithms that `predict` labels and `transform` new
-  input features into a space of lower dimension. See [Transformers
-  that also predict](@ref) for an example.
 
 ## Static models (models that do not generalize)
 

docs/src/tuning_models.md

@@ -6,13 +6,13 @@ advanced keyword options.
 
 tuning strategy | notes |package to import | package providing the core algorithm
 ----------------|-------|------------------|----------------------------------
-[`Grid`](@ref)`(goal=nothing, resolution=10)` | shuffled by default; `goal` is upper bound for number of grid points | MLJ.jl or MLJTuning.jl | [MLJTuning.jl](https://github.com/FluxML/model-zoo)
-[`RandomSearch`](@ref)`(rng=GLOBAL_RNG)` | with customizable priors |MLJ.jl or MLJTuning.jl   | [MLJTuning.jl](https://github.com/FluxML/model-zoo)
+[`Grid`](@ref)`(goal=nothing, resolution=10)` | shuffled by default; `goal` is upper bound for number of grid points | MLJ.jl or MLJTuning.jl | [MLJTuning.jl](https://github.com/JuliaAI/MLJTuning.jl)
+[`RandomSearch`](@ref)`(rng=GLOBAL_RNG)` | with customizable priors |MLJ.jl or MLJTuning.jl   | [MLJTuning.jl](https://github.com/JuliaAI/MLJTuning.jl)
 [`LatinHypercube`](@ref)`(rng=GLOBAL_RNG)` | with discrete parameter support | MLJ.jl or MLJTuning.jl | [LatinHypercubeSampling](https://github.com/MrUrq/LatinHypercubeSampling.jl)
 `MLJTreeParzenTuning()` | See this [example](https://github.com/IQVIA-ML/TreeParzen.jl/blob/master/docs/examples/simple_mlj_demo/simple_mlj_demo.md) for usage | TreeParzen.jl | [TreeParzen.jl](https://github.com/IQVIA-ML/TreeParzen.jl) (port to Julia of [hyperopt](http://hyperopt.github.io/hyperopt/))
 `ParticleSwarm(n_particles=3, rng=GLOBAL_RNG)` | Standard Kennedy-Eberhart algorithm, plus discrete parameter support | MLJParticleSwarmOptimization.jl | [MLJParticleSwarmOptimization.jl](https://github.com/JuliaAI/MLJParticleSwarmOptimization.jl/)
 `AdaptiveParticleSwarm(n_particles=3, rng=GLOBAL_RNG)` | Zhan et al. variant with automated swarm coefficient updates, plus discrete parameter support | MLJParticleSwarmOptimization.jl | [MLJParticleSwarmOptimization.jl](https://github.com/JuliaAI/MLJParticleSwarmOptimization.jl/)
-`Explicit()` | For an [explicit list](@ref explicit) of models of varying type | MLJ.jl or MLJTuning.jl | [MLJTuning.jl](https://github.com/FluxML/model-zoo)
+`Explicit()` | For an [explicit list](@ref explicit) of models of varying type | MLJ.jl or MLJTuning.jl | [MLJTuning.jl](https://github.com/JuliaAI/MLJTuning.jl)
 
 Below we illustrate hyperparameter optimization using the
 [`Grid`](@ref), [`RandomSearch`](@ref), [`LatinHypercube`](@ref) and

test/exported_names.jl

@@ -35,7 +35,7 @@ Save()
 
 # MLJFlow
 
-MLFlowLogger
+MLJFlow.Logger
 
 # StatisticalMeasures