Julia interface to CatBoost. This library is a wrapper CatBoost's Python package via PythonCall.jl.
For a nice introduction to the package, see the examples.
This package is available in the Julia General Registry. You can install it with either of the following commands:
pkg> add CatBoost
julia> using Pkg; Pkg.add("CatBoost")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 # modulemodule 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 # moduleBy 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:
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.