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https://github.com/juliaai/catboost.jl
Julia wrapper of the python library CatBoost for boosted decision trees
https://github.com/juliaai/catboost.jl
Last synced: 7 days ago
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Julia wrapper of the python library CatBoost for boosted decision trees
- Host: GitHub
- URL: https://github.com/juliaai/catboost.jl
- Owner: JuliaAI
- License: mit
- Created: 2021-05-04T16:56:43.000Z (over 3 years ago)
- Default Branch: main
- Last Pushed: 2024-06-18T20:30:38.000Z (5 months ago)
- Last Synced: 2024-07-08T21:29:18.954Z (4 months ago)
- Language: Julia
- Size: 236 KB
- Stars: 11
- Watchers: 18
- Forks: 3
- Open Issues: 5
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
README
# CatBoost.jl
[![Build Status][build-img]][build-url] [![CodeCov][codecov-img]][codecov-url] [](https://JuliaAI.github.io/CatBoost.jl/dev)
[build-img]: https://github.com/JuliaAI/CatBoost.jl/workflows/CI/badge.svg
[build-url]: https://github.com/JuliaAI/CatBoost.jl/actions
[codecov-img]: https://codecov.io/gh/JuliaAI/CatBoost.jl/branch/main/graph/badge.svg
[codecov-url]: https://codecov.io/github/JuliaAI/CatBoost.jl
Julia interface to [CatBoost](https://catboost.ai/). This library is a wrapper CatBoost's Python package via [PythonCall.jl](https://github.com/cjdoris/PythonCall.jl).
For a nice introduction to the package, see the [examples](https://github.com/JuliaAI/CatBoost.jl/blob/main/examples/).
# Installation
This package is available in the Julia General Registry. You can install it with either of the following commands:
```
pkg> add CatBoost
```
```julia
julia> using Pkg; Pkg.add("CatBoost")
```
## Example
```julia
module Regression
using CatBoost
using PythonCall
train_data = PyList([[1, 4, 5, 6], [4, 5, 6, 7], [30, 40, 50, 60]])
eval_data = PyList([[2, 4, 6, 8], [1, 4, 50, 60]])
train_labels = PyList([10, 20, 30])
# Initialize CatBoostRegressor
model = CatBoostRegressor(iterations = 2, learning_rate = 1, depth = 2)
# Fit model
fit!(model, train_data, train_labels)
# Get predictions
preds = predict(model, eval_data)
end # module
```
## MLJ Example
```julia
module Regression
using CatBoost.MLJCatBoostInterface
using DataFrames
using MLJBase
# Initialize data
train_data = DataFrame([[1, 4, 30], [4, 5, 40], [5, 6, 50], [6, 7, 60]], :auto)
train_labels = [10.0, 20.0, 30.0]
eval_data = DataFrame([[2, 1], [4, 4], [6, 50], [8, 60]], :auto)
# Initialize CatBoostClassifier
model = CatBoostRegressor(; iterations=2, learning_rate=1.0, depth=2)
mach = machine(model, train_data, train_labels)
# Fit model
MLJBase.fit!(mach)
# Get predictions
preds_class = MLJBase.predict(mach, eval_data)
end # module
```
# Restricting Python catboost version
By default, `CatBoost.jl` installs the latest compatible version of `catboost` (version `>=1.1`) in your current `CondaPkg.jl` environment. To install a specific version, create a `CondaPkg.toml` file using `CondaPkg.jl`. Below is an example for specifying `catboost` version `v1.1`:
```julia
using CondaPkg
CondaPkg.add("catboost"; version="=1.1")
```
This will create a `CondaPkg.toml` file in your current envrionment with the restricted `catboost` version. For more information on managing Conda environments with `CondaPkg.jl`, refer to the [official documentation](https://github.com/cjdoris/CondaPkg.jl).