diff --git a/.gitignore b/.gitignore index 0f23164..4411b0e 100644 --- a/.gitignore +++ b/.gitignore @@ -25,4 +25,9 @@ Manifest.toml **/.DS_Store # ignore .vscode -.vscode/ \ No newline at end of file +.vscode/ +simulations/.CondaPkg/ + +simulations/ + +examples/rsl_example.jl diff --git a/examples/rsl_example.jl b/examples/rsl_example.jl deleted file mode 100644 index 75111f5..0000000 --- a/examples/rsl_example.jl +++ /dev/null @@ -1,79 +0,0 @@ -using Revise -using Graphs -using Random -using LinearAlgebra -using DataFrames -using Plots -using GraphRecipes -using Statistics -using Distributions -using CSV -using DelimitedFiles -using RecursiveCausalDiscovery -using CausalInference -using BenchmarkTools - - -function get_pc_skeleton(t, p::Float64, test::typeof(gausscitest)) - Tables.istable(t) || throw(ArgumentError("Argument does not support Tables.jl")) - X = Tables.matrix(t) - N, n = size(X) - C = Statistics.cor(X, dims = 1) - g_skeleton, _ = CausalInference.skeleton(n, test, (C, N), quantile(Normal(), 1 - p / 2)) - return g_skeleton -end - - - - -# adj_mat = gen_er_dag_adj_mat(10, 0.3) -# df = gen_gaussian_data(adj_mat, 1000) -# ix = [1, 2, 3] -# A = [1, 1, 1] - -# deforder = Base.Order.ord(isless, identity, nothing, Base.Order.Forward) - -# ix = [1 4; 2 5; 3 6] -# A = [2 1; 4 3; 6 5] -# dims = nothing -# Base.Order.Perm(deforder, vec(A)) -# sort!(ix; Base.Sort.DEFAULT_UNSTABLE, order = Base.Order.Perm(deforder, vec(A)), nothing, dims=2) - -# sort!(ix, dims=1) - -# sortperm([1,2;3,4;5,6], dims=1) -# sortperm([1], dims=1) - -# load csv as matrix -folder_name = "../test/n_200/" -matrix_data = CSV.read(joinpath(folder_name, "data.csv"), Tables.matrix) -table_data = Tables.table(matrix_data) - -sig_level = 2 / size(matrix_data, 2)^2 -ci_test = (x::Int, y::Int, z::Vector{Int}, data::Matrix{Float64}) -> fisher_z(x, y, z, data, sig_level) - -precision_mat = inv(cor(matrix_data)) -opt_ci_test = (x::Int, y::Int, z::Vector{Int}, data::Matrix{Float64}) -> opt_fisher_z(x, y, z, size(matrix_data, 1), precision_mat, sig_level) - -# learn the skeleton using RSL -rsl_skeleton = learn_and_get_skeleton(table_data, ci_test, mkbd_ci_test=opt_ci_test) -rsl_adj_mat = adjacency_matrix(rsl_skeleton) - -# learn the skeleton using PC (CausalInference.jl) -pc_skeleton = get_pc_skeleton(table_data, sig_level, gausscitest) -pc_adj_mat = adjacency_matrix(pc_skeleton) - -# load true adjacency matrix from csv -true_adj_mat = Int.(CSV.read(joinpath(folder_name, "true_adj_mat.csv"), Tables.matrix, header=false)) -true_skeleton = Graph(true_adj_mat) - -# calculate f1 score -rsl_f1 = f1_score(true_skeleton, rsl_skeleton) -pc_f1 = f1_score(true_skeleton, pc_skeleton) - -println("RSL F1 Score: ", rsl_f1) -println("PC F1 Score: ", pc_f1) - -#@benchmark learn_and_get_skeleton(table_data, ci_test, mkbd_ci_test=opt_ci_test) -#@benchmark get_pc_skeleton(table_data, sig_level, gausscitest) - diff --git a/examples/true_adj_mat.csv b/examples/true_adj_mat.csv deleted file mode 100644 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