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⭐️ Better documentation
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‎.github/workflows/documenter.yml‎

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name: Documentation
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on:
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push:
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branches:
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- dev
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- instate-docs
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tags: '*'
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pull_request:
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jobs:
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build:
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permissions:
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contents: write
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pull-requests: read
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statuses: write
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runs-on: ubuntu-latest
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steps:
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- uses: actions/checkout@v4
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- uses: julia-actions/setup-julia@v2
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with:
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version: '1.10'
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- uses: julia-actions/cache@v1
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- name: Install dependencies
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run: julia --project=docs/ -e 'using Pkg; Pkg.develop(PackageSpec(path=pwd())); Pkg.instantiate()'
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- name: Build and deploy
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env:
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GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }} # If authenticating with GitHub Actions token
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DOCUMENTER_KEY: ${{ secrets.DOCUMENTER_KEY }} # If authenticating with SSH deploy key
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run: julia --project=docs/ docs/make.jl

‎docs/make.jl‎

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pages = [
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"Introduction" => "index.md",
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"Transformers" => Any[
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"Numerical Transformers"=>"transformers/numerical.md",
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"Classical Encoders"=>"transformers/classical.md",
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"Neural-based Encoders"=>"transformers/neural.md",
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"Contrast Encoders"=>"transformers/contrast.md",

‎docs/src/index.md‎

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```
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## Available Transformers
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In `MLJTransforms` we define "encoders" to encompass models that specifically operate by encoding categorical variables; meanwhile, "transformers" refers to models that apply more generic transformations on columns that are not necessarily categorical. We define the following taxonomy for different models founds in `MLJTransforms`:
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| Genre | Definition |
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|:----------:|:----------:|
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| **Classical Encoders** | Well known and commonly used categorical encoders |
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| **Neural-based Encoders** | Categorical encoders based on neural networks |
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| **Contrast Encoders** | Categorical encoders that could be modeled by a contrast matrix |
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| **Utility Encoders** | Categorical encoders meant to be used as preprocessors for other encoders or models |
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| **Other Transformers** | More generic transformers that go beyond categorical encoding |
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In `MLJTransforms` we denote transformers that operate on columns with `Continuous` and/or `Count` [scientific types](https://juliaai.github.io/ScientificTypes.jl/dev/) as numerical transformers. Meanwhile, categorical transformers operate on `Multiclass` and/or `OrderedFactor` [scientific types](https://juliaai.github.io/ScientificTypes.jl/dev/). Most categorical transformers in this package operate by converting categorical values into numerical values or vectors, and are therefore considered categorical encoders.
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Based on this, we categorize the methods as follows, with further distinctions for categorical encoders:
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| **Category** | **Description** |
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|:---------------------------:|:-------------------------------------------------------------------------------:|
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| **Numerical Transformers** | Transformers that operate on `Continuous` or `Count` columns in a given dataset.|
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| **Classical Encoders** | Widely recognized and frequently utilized categorical encoders. |
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| **Neural-based Encoders** | Categorical encoders based on neural networks. |
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| **Contrast Encoders** | Categorical encoders modeled via a contrast matrix. |
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| **Utility Encoders** | Categorical encoders meant to be used as preprocessors for other encoders or models.|
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| **Other Transformers** | Transformers that fall into other categories. |

‎docs/src/transformers/contrast.md‎

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| [DummyEncoder](@ref) | Encodes by comparing each level to the reference level, intercept being the cell mean of the reference group |
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| [SumEncoder](@ref) | Encodes by comparing each level to the reference level, intercept being the grand mean |
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| [HelmertEncoder](@ref) | Encodes by comparing levels of a variable with the mean of the subsequent levels of the variable
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|
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| [HelmertEncoder](@ref) | Encodes by comparing levels of a variable with the mean of the subsequent levels of the variable |
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| [ForwardDifferenceEncoder](@ref) | Encodes by comparing adjacent levels of a variable (each level minus the next level)
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|
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| [ContrastEncoder](@ref) | Allows defining a custom contrast encoder via a contrast matrix |
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| [HypothesisEncoder](@ref) | Allows defining a custom contrast encoder via a hypothesis matrix |
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‎docs/src/transformers/neural.md‎

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Neural-based Encoders include ategorical encoders based on neural networks:
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Neural-based Encoders include categorical encoders based on neural networks:
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| Transformer | Brief Description |
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|:----------:|:----------:|

‎docs/src/transformers/numerical.md‎

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Other Transformers include more generic transformers that go beyond categorical encoding
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| Transformer | Brief Description |
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|:----------:|:----------:|
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| [Standardizer](@ref) | Transforming columns of numerical features by standardization |
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| [BoxCoxTransformer](@ref) | Transforming columns of numerical features by BoxCox transformation |
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| [UnivariateBoxCoxTransformer](@ref) | Apply BoxCox transformation given a single vector |
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| [InteractionTransformer](@ref) | Transforming columns of numerical features to create new interaction features |
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| [UnivariateDiscretizer](@ref) | Discretize a continuous vector into an ordered factor |
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```@docs
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MLJTransforms.Standardizer
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```
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```@docs
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MLJTransforms.InteractionTransformer
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```
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```@docs
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MLJTransforms.BoxCoxTransformer
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```
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```@docs
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MLJTransforms.UnivariateDiscretizer
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```

‎docs/src/transformers/others.md‎

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Other Transformers include more generic transformers that go beyond categorical encoding
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Transformers that operate on columns with general or specialized scientific types.
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| Transformer | Brief Description |
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|:----------:|:----------:|
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| [FillImputer](@ref) | Fill missing values of any features belonging to any scientific type |
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| [Standardizer](@ref) | Transforming columns of numerical features by standardization |
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| [BoxCoxTransformer](@ref) | Transforming columns of numerical features by BoxCox transformation |
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| [InteractionTransformer](@ref) | Transforming columns of numerical features by creating new interaction features |
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| [UnivariateBoxCoxTransformer](@ref) | Apply BoxCox transformation given a single vector |
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| [UnivariateDiscretizer](@ref) | Discretize a continuous vector into an ordered factor |
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| [FillImputer](@ref) | Fill missing values of features belonging to any scientific type |
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| [UnivariateTimeTypeToContinuous](@ref) | Transform a vector of time type into continuous type |
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```@docs
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MLJTransforms.FillImputer
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```
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```@docs
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MLJTransforms.Standardizer
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```
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```@docs
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MLJTransforms.BoxCoxTransformer
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```
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```@docs
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MLJTransforms.InteractionTransformer
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```
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```@docs
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MLJTransforms.UnivariateDiscretizer
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```
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```@docs
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MLJTransforms.UnivariateTimeTypeToContinuous

‎docs/src/transformers/utility.md‎

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| Transformer | Brief Description |
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|:----------:|:----------:|
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| [CardinalityReducer](@ref) | Reduce cardinality of high cardinality features by grouping infrequent categories |
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| [MissingnessEncoder](@ref) | Encode missing values of categorical columns into new values |
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| [CardinalityReducer](@ref) | Reduce cardinality of high cardinality categorical features by grouping infrequent categories |
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| [MissingnessEncoder](@ref) | Encode missing values of categorical features into new values |
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```@docs
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MLJTransforms.CardinalityReducer

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