consistent lambda selection

WGUNDERWOOD · Oct 19, 2023 · 1fdd3f3 · 1fdd3f3
1 parent fdafa0c
commit 1fdd3f3
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Showing 5 changed files with 8 additions and 9 deletions.
diff --git a/README.md b/README.md
@@ -75,7 +75,7 @@ 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, n_subsample, debias_order, X, Y)
+lambda = select_lifetime_gcv(lambdas, n_trees, X, Y, debias_order, n_subsample)
 println("\nlambda chosen by GCV: ", lambda)
 
 # fit and evaluate a Mondrian random forest

diff --git a/replication/readme_examples/readme_example.jl b/replication/readme_examples/readme_example.jl
@@ -27,7 +27,7 @@ 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, n_subsample, debias_order, X, Y)
+lambda = select_lifetime_gcv(lambdas, n_trees, X, Y, debias_order, n_subsample)
 println("\nlambda chosen by GCV: ", lambda)
 
 # fit and evaluate a Mondrian random forest

diff --git a/src/lifetime_gcv.jl b/src/lifetime_gcv.jl
@@ -2,9 +2,9 @@
 Select the lifetime parameter for a (debiased) Mondrian random forest
 using generalized cross-validation.
 """
-function select_lifetime_gcv(lambdas::Vector{Float64}, n_trees::Int, n_subsample::Int,
-                             debias_order::Int, X_data::Vector{NTuple{d,Float64}},
-                             Y_data::Vector{Float64}) where {d}
+function select_lifetime_gcv(lambdas::Vector{Float64}, n_trees::Int,
+                             X_data::Vector{NTuple{d,Float64}}, Y_data::Vector{Float64},
+                             debias_order::Int, n_subsample::Int) where {d}
     n_lambdas = length(lambdas)
     gcvs = [NaN for _ in 1:n_lambdas]
     for l in 1:n_lambdas

diff --git a/src/lifetime_polynomial.jl b/src/lifetime_polynomial.jl
@@ -2,9 +2,8 @@
 Select the lifetime parameter for a (debiased) Mondrian random forest
 using polynomial estimation.
 """
-# TODO consistency with GCV method
 function select_lifetime_polynomial(X_data::Vector{NTuple{d,Float64}}, Y_data::Vector{Float64},
-                                    debias_order::Int) where {d}
+                                    debias_order::Int=0) where {d}
     n = length(X_data)
     derivative_estimates = get_derivative_estimates_polynomial(X_data, Y_data, debias_order)
     sigma2_hat = get_variance_estimate_polynomial(X_data, Y_data, debias_order)

diff --git a/test/test_lifetime_gcv.jl b/test/test_lifetime_gcv.jl
@@ -13,8 +13,8 @@
             n_trees = 100
             n_subsample = 20
             lambdas = collect(range(1.0, 20.1, step=1))
-            lambda = select_lifetime_gcv(lambdas, n_trees, n_subsample,
-                                         debias_order, X_data, Y_data)
+            lambda = select_lifetime_gcv(lambdas, n_trees, X_data, Y_data,
+                                         debias_order, n_subsample)
         end
     end
 end