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https://github.com/wgunderwood/mondrianforests.jl
Mondrian random forests in Julia
https://github.com/wgunderwood/mondrianforests.jl
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Mondrian random forests in Julia
- Host: GitHub
- URL: https://github.com/wgunderwood/mondrianforests.jl
- Owner: WGUNDERWOOD
- License: gpl-3.0
- Created: 2023-04-20T18:12:51.000Z (over 1 year ago)
- Default Branch: main
- Last Pushed: 2024-08-27T08:36:49.000Z (5 months ago)
- Last Synced: 2024-09-27T20:06:25.849Z (4 months ago)
- Language: Julia
- Homepage:
- Size: 3.96 MB
- Stars: 7
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE.md
Awesome Lists containing this project
README
# MondrianForests.jl 
Mondrian random forests in Julia
[](https://github.com/WGUNDERWOOD/MondrianForests.jl/actions/workflows/CI.yml)
[](https://www.gnu.org/licenses/gpl-3.0)
[](https://codecov.io/gh/WGUNDERWOOD/MondrianForests.jl/)
[](https://WGUNDERWOOD.github.io/MondrianForests.jl/stable)
[](https://WGUNDERWOOD.github.io/MondrianForests.jl/dev)
[](https://github.com/JuliaTesting/Aqua.jl)
## Introduction
This repository provides implementations of Mondrian random forests in Julia,
based on methods detailed in
[Cattaneo, Klusowski and Underwood, 2023, arXiv:2310:09702](https://arxiv.org/abs/2310.09702).
This package provides:
- Fitting (debiased) Mondrian random forests
- Selecting a lifetime parameter with polynomial estimation or generalized cross-validation
### Branches
The main branch contains stable versions.
Other branches may be unstable,
and are for development purposes only.
### License
This repository and its included Julia package are licensed under
[GPLv3](http://gplv3.fsf.org/).
## Julia package
The Julia package is named **MondrianForests.jl**
### Installation
From the Julia General registry:
```julia
using Pkg
Pkg.add("MondrianForests")
```
### Usage
```julia
using MondrianForests
# sample a two-dimensional Mondrian tree
d = 2
lambda = 2.0
tree = MondrianTree(d, lambda)
println()
show(tree)
println()
# generate some data
# covariates X_data are two-dimensional
# response Y_data is one-dimensional
# true regression function is zero
n_data = 100
data = MondrianForests.generate_uniform_data_uniform_errors(d, n_data)
X_data = data["X"]
Y_data = data["Y"]
println("covariates: ")
display(X_data[1:5])
println("\nresponses: ")
display(Y_data[1:5])
# select a lifetime parameter
# with generalized cross-validation
n_trees = 50
n_subsample = 30
debias_order = 0
lambdas = collect(range(0.5, 10.0, step=0.5))
lambda = select_lifetime_gcv(lambdas, n_trees, X_data, Y_data, debias_order, n_subsample)
println("\nlambda chosen by GCV: ", lambda)
# fit and evaluate a Mondrian random forest
x_evals = [(0.5, 0.5), (0.2, 0.8)]
estimate_var = true
forest = MondrianForest(lambda, n_trees, x_evals, X_data, Y_data, estimate_var)
println("\nestimated regression function:")
display(forest.mu_hat)
println("\nestimated estimator variance:")
display(forest.Sigma_hat)
println("\nestimated confidence band:")
display(forest.confidence_band)
# fit and evaluate a debiased Mondrian random forest
debiased_forest = DebiasedMondrianForest(lambda, n_trees, x_evals, debias_order,
X_data, Y_data, estimate_var)
println("\ndebiased estimated regression function:")
display(debiased_forest.mu_hat)
println("\ndebiased estimated estimator variance:")
display(debiased_forest.Sigma_hat)
println("\ndebiased estimated confidence band:")
display(debiased_forest.confidence_band)
```
### Dependencies
- Distributions
- Random
- Suppressor
- Test
### Documentation
Documentation for the **MondrianForests** package is available on
[the web](https://wgunderwood.github.io/MondrianForests.jl/stable/).