Traits (#18)

* remove dependence of is_wrapper trait on base model.

* bug fixes and doc build

* fix typo in docstring

Co-authored-by: Anthony Blaom, PhD <[email protected]>

---------

Co-authored-by: Anthony Blaom, PhD <[email protected]>

Author: OkonSamuel

README.md

@@ -1,7 +1,24 @@
 # FeatureSelection.jl
 
-| Linux | Coverage | Code Style
-| :------------ | :------- | :------------- |
-| [![Build Status](https://github.com/JuliaAI/FeatureSelection.jl/workflows/CI/badge.svg)](https://github.com/JuliaAI/FeatureSelection.jl/actions) | [![Coverage](https://codecov.io/gh/JuliaAI/FeatureSelection.jl/branch/master/graph/badge.svg)](https://codecov.io/github/JuliaAI/FeatureSelection.jl?branch=dev) | [![Code Style: Blue](https://img.shields.io/badge/code%20style-blue-4495d1.svg)](https://github.com/invenia/BlueStyle) |
+| Linux | Coverage | Documentation | Code Style
+| :------------ | :------- | :------------- | :------------- |
+| [![Build Status](https://github.com/JuliaAI/FeatureSelection.jl/workflows/CI/badge.svg)](https://github.com/JuliaAI/FeatureSelection.jl/actions) | [![Coverage](https://codecov.io/gh/JuliaAI/FeatureSelection.jl/branch/master/graph/badge.svg)](https://codecov.io/github/JuliaAI/FeatureSelection.jl?branch=dev) | [![Stable](https://img.shields.io/badge/docs-stable-blue.svg)](https://juliaai.github.io/FeatureSelection.jl/dev/) | [![Code Style: Blue](https://img.shields.io/badge/code%20style-blue-4495d1.svg)](https://github.com/invenia/BlueStyle) |
 
 Repository housing feature selection algorithms for use with the machine learning toolbox [MLJ](https://juliaai.github.io/MLJ.jl/dev/).
+
+This package provides a collection of feature selection algorithms designed for use with MLJ, a powerful machine learning toolbox in Julia. It aims to facilitate the process of selecting the most relevant features from your datasets, enhancing the performance and interpretability of your machine learning models.
+
+## Key Features
+- Integration with MLJ: Seamlessly integrates with MLJ's extensive suite of tools and models.
+- Variety of Algorithms: Includes multiple feature selection algorithms to suit different types of data and models.
+- User-friendly: Easy to use with clear documentation and examples.
+
+## Getting Started
+To get started with this package, refer to the documentation for installation instructions, usage guides, and API references.
+
+## Contributing
+Contributions are welcome! Please refer to MLJ contributing [guidelines](https://github.com/JuliaAI/MLJ.jl/blob/dev/CONTRIBUTING.md) for more information.
+
+## License
+This project is licensed under the MIT License - see the [LICENSE](LICENSE) file for details.
+

docs/make.jl

@@ -30,5 +30,6 @@ makedocs(;
 deploydocs(;
     deploy_config = Documenter.GitHubActions(),
     repo="github.com/JuliaAI/FeatureSelection.jl.git",
+    devbranch="dev",
     push_preview=true
 )

docs/src/api.md

@@ -6,4 +6,9 @@ CurrentModule = FeatureSelection
 ```@docs
 FeatureSelector
 RecursiveFeatureElimination
+```
+# Internal Utils
+```@docs
+abs_last
+score_features!
 ```

docs/src/index.md

@@ -52,13 +52,13 @@ end
 ```
 ```@example example1
 using MLJ, FeatureSelection, StableRNGs
-rng = StableRNG(10)
+rng = StableRNG(123)
 A = rand(rng, 50, 10)
 X = MLJ.table(A) # features
-y = @views(
-    10 .* sin.(
-        pi .* A[:, 1] .* A[:, 2]
-    ) .+ 20 .* (A[:, 3] .- 0.5).^ 2 .+ 10 .* A[:, 4] .+ 5 * A[:, 5]
+ y = @views(
+        10 .* sin.(
+            pi .* A[:, 1] .* A[:, 2]
+        ) + 20 .* (A[:, 3] .- 0.5).^ 2 .+ 10 .* A[:, 4] .+ 5 * A[:, 5]
 ) # target
 ```
 Now we that we have our data, we can create our recursive feature elimination model and 
@@ -74,51 +74,49 @@ fit!(mach)
 ```
 We can inspect the feature importances in two ways:
 ```jldoctest
-julia> report(mach).ranking
-10-element Vector{Int64}:
- 1
- 1
- 1
- 1
- 1
- 2
- 3
- 4
- 5
- 6
+julia> report(mach).scores
+Dict{Symbol, Int64} with 10 entries:
+  :x9  => 4
+  :x2  => 6
+  :x5  => 6
+  :x6  => 3
+  :x7  => 2
+  :x3  => 6
+  :x8  => 1
+  :x4  => 6
+  :x10 => 5
+  :x1  => 6
 
 julia> feature_importances(mach)
 10-element Vector{Pair{Symbol, Int64}}:
-  :x1 => 6
-  :x2 => 5
-  :x3 => 4
-  :x4 => 3
-  :x5 => 2
-  :x6 => 1
-  :x7 => 1
+  :x9 => 4
+  :x2 => 6
+  :x5 => 6
+  :x6 => 3
+  :x7 => 2
+  :x3 => 6
   :x8 => 1
-  :x9 => 1
- :x10 => 1
+  :x4 => 6
+ :x10 => 5
+  :x1 => 6
 ```
-Note that a variable with lower rank has more significance than a variable with higher rank; while a variable with higher feature importance is better than a variable with lower feature importance.
-
 We can view the important features used by our model by inspecting the `fitted_params` 
 object.
 ```jldoctest
 julia> p = fitted_params(mach)
-(features_left = [:x1, :x2, :x3, :x4, :x5],
+(features_left = [:x4, :x2, :x1, :x5, :x3],
  model_fitresult = (forest = Ensemble of Decision Trees
 Trees:      100
-Avg Leaves: 25.26
-Avg Depth:  8.36,),)
+Avg Leaves: 25.3
+Avg Depth:  8.01,),)
 
 julia> p.features_left
 5-element Vector{Symbol}:
- :x1
- :x2
- :x3
  :x4
+ :x2
+ :x1
  :x5
+ :x3
 ```
 We can also call the `predict` method on the fitted machine, to predict using a 
 random forest regressor trained using only the important features, or call the `transform` 
@@ -157,16 +155,16 @@ julia> fitted_params(self_tuning_rfe_mach).best_fitted_params.features_left
 
 julia> feature_importances(self_tuning_rfe_mach)
 10-element Vector{Pair{Symbol, Int64}}:
-  :x1 => 6
-  :x2 => 5
-  :x3 => 4
-  :x4 => 3
-  :x5 => 2
-  :x6 => 1
+  :x9 => 2
+  :x2 => 6
+  :x5 => 6
+  :x6 => 4
   :x7 => 1
-  :x8 => 1
-  :x9 => 1
- :x10 => 1
+  :x3 => 6
+  :x8 => 5
+  :x4 => 6
+  :x10 => 3
+  :x1 => 6
 ```
 and call `predict` on the tuned model machine as shown below
 ```@example example1