docs

README.md

@@ -1,4 +1,4 @@
-# MondrianForests.jl <img src="docs/src/assets/logo.svg" alt="Mondrian forests logo" align="right" width=220 />
+# MondrianForests.jl <img src="docs/src/assets/logo.svg" alt="Mondrian forests logo" align="right" width=200 />
 
 Mondrian random forests in Julia
 
@@ -9,15 +9,14 @@ Mondrian random forests in Julia
 
 ## Introduction
 
-This repository provides implementations of Mondrian random forests in Julia.
-This code is based on methods detailed in
+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 Mondrian random forests
 - Fitting debiased Mondrian random forests
-- Selecting the lifetime parameter with polynomial estimation
-- Selecting the lifetime parameter with generalized cross-validation
+- Selecting a lifetime parameter with polynomial estimation or generalized cross-validation
 
 ### Branches
 
@@ -57,31 +56,31 @@ show(tree)
 println()
 
 # generate some data
-# covariates X are two-dimensional
-# response Y is one-dimensional
+# 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["X"]
-Y = data["Y"]
+X_data = data["X"]
+Y_data = data["Y"]
 println("covariates: ")
-display(X[1:5])
+display(X_data[1:5])
 println("\nresponses: ")
-display(Y[1:5])
+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, Y, debias_order, n_subsample)
+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, Y, estimate_var)
+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:")
@@ -90,7 +89,8 @@ 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, Y, estimate_var)
+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:")

docs/src/index.md

@@ -1 +1,106 @@
 # MondrianForests
+
+Mondrian random forests in Julia
+
+## 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 Mondrian random forests
+- 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/).

src/forest.jl

@@ -53,7 +53,8 @@ X_data = data["X"]
 Y_data= data["Y"]
 estimate_var = true
 significance_level = 0.05
-forest = MondrianForest(lambda, n_trees, x_evals, X_data, Y_data, estimate_var, significance_level)
+forest = MondrianForest(lambda, n_trees, x_evals, X_data, Y_data,
+                        estimate_var, significance_level)
 ```
 """
 function MondrianForest(lambda::Float64, n_trees::Int, x_evals::Vector{NTuple{d,Float64}},