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Merge pull request #19 from JuliaAI/docs
🌟 Initialize 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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- main
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tags: '*'
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pull_request:
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concurrency:
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# Skip intermediate builds: always.
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# Cancel intermediate builds: only if it is a pull request build.
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group: ${{ github.workflow }}-${{ github.ref }}
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cancel-in-progress: ${{ startsWith(github.ref, 'refs/pull/') }}
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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

.gitignore

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/*.jl
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scratchpad/
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examples/test.jl
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catboost_info/**
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/catboost_info
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/catboost_info
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/docs/src/tutorials/adult_example/.CondaPkg
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/docs/src/tutorials/adult_example/catboost_info

Project.toml

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name = "MLJTransforms"
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uuid = "23777cdb-d90c-4eb0-a694-7c2b83d5c1d6"
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authors = ["Essam <[email protected]> and contributors"]
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version = "0.1.2"
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[deps]

README.md

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# MLJTransforms.jl
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![logg](https://github.com/JuliaAI/MLJTransforms/assets/49572294/8d119672-e641-4971-884d-dcb793ba0c05)
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Preprocessing, transformation and encoding techniques for MLJ.
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A Julia package providing a wide range of categorical encoders and data transformers to be used with the [MLJ](https://juliaai.github.io/MLJ.jl/dev/) package.
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[![Build Status](https://github.com/JuliaAI/Imbalance.jl/workflows/CI/badge.svg)](https://github.com/JuliaAI/Imbalance.jl/actions)
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[![Docs](https://img.shields.io/badge/docs-dev-blue.svg)](https://juliaai.github.io/MLJTransforms.jl/dev/)
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## Quick Start
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For the following demo, you will need to additionally run `Pkg.add("RDatasets")`.
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```julia
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using MLJ, MLJTransforms
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import RDatasets
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# 1. Load Data
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X = RDatasets.dataset("HSAUR", "Forbes2000");
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# 2. Load the model
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FrequencyEncoder = @load FrequencyEncoder pkg="MLJTransforms"
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encoder = FrequencyEncoder(
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features=[:Country, :Category], # The categorical columns to select
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ignore=false, # Whether to exclude or include selected columns
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ordered_factor = false, # Whether to also encode columns of ordered factor elements
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normalize=true # Whether to normalize the frequencies used for encoding
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)
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# 3. Wrap it in a machine and fit
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mach = fit!(machine(encoder, X))
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Xnew = transform(mach, X)
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```
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See the full [documentation](https://juliaai.github.io/MLJTransforms.jl/dev/) for more information and a [full list](https://juliaai.github.io/MLJTransforms.jl/dev/all_transformers) of transformers in this package.
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## 👥 Credits
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This package was created by [Essam Wisam](https://github.com/JuliaAI) as a Google Summer of Code project on categorical encoding, under the mentorship of [Anthony Blaom](https://ablaom.github.io). Subsequently, the package was expanded to include data transformation methods beyond categorical encoders that previously existed in other packages.

docs/.gitignore

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build/
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site/

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