From 300209ab5c888e40b64cc10aa3399fc811b27b0e Mon Sep 17 00:00:00 2001 From: Naseweisssss Date: Mon, 18 Nov 2024 14:39:28 +0000 Subject: [PATCH] keep consistent --- .../ProbabilisticGraphicalModels.jl | 12 + .../ProbabilisticGraphicalModels/bayesnet.jl | 109 +------- .../ProbabilisticGraphicalModels/bayesnet.jl | 233 +----------------- 3 files changed, 24 insertions(+), 330 deletions(-) diff --git a/src/experimental/ProbabilisticGraphicalModels/ProbabilisticGraphicalModels.jl b/src/experimental/ProbabilisticGraphicalModels/ProbabilisticGraphicalModels.jl index 4785bf151..17475102e 100644 --- a/src/experimental/ProbabilisticGraphicalModels/ProbabilisticGraphicalModels.jl +++ b/src/experimental/ProbabilisticGraphicalModels/ProbabilisticGraphicalModels.jl @@ -6,4 +6,16 @@ using Distributions include("bayesnet.jl") +export BayesianNetwork, + Factor, + create_factor, + multiply_factors, + marginalize, + add_stochastic_vertex!, + add_deterministic_vertex!, + add_edge!, + condition, + decondition, + variable_elimination + end diff --git a/src/experimental/ProbabilisticGraphicalModels/bayesnet.jl b/src/experimental/ProbabilisticGraphicalModels/bayesnet.jl index 8f237b389..b5a8bc1a9 100644 --- a/src/experimental/ProbabilisticGraphicalModels/bayesnet.jl +++ b/src/experimental/ProbabilisticGraphicalModels/bayesnet.jl @@ -4,7 +4,7 @@ A structure representing a Bayesian Network. """ struct BayesianNetwork{V,T,F} - graph::SimpleDiGraph{T} + graph::SimpleGraph{T} "names of the variables in the network" names::Vector{V} "mapping from variable names to ids" @@ -25,7 +25,7 @@ end function BayesianNetwork{V}() where {V} return BayesianNetwork( - SimpleDiGraph{Int}(), # by default, vertex ids are integers + SimpleGraph{Int}(), # by default, vertex ids are integers V[], Dict{V,Int}(), Dict{V,Any}(), @@ -164,36 +164,13 @@ Perform ancestral sampling on a Bayesian network to generate one sample from the Ancestral sampling works by: 1. Finding a topological ordering of the nodes 2. Sampling from each node in order, using the already-sampled parent values for conditional distributions - -### Return Value -The function returns a `Dict{V, Any}` where: -- Each key is a variable name (of type `V`) in the Bayesian Network. -- Each value is the sampled value for that variable, which can be of any type (`Any`). - -This dictionary represents a single sample from the joint distribution of the Bayesian Network, capturing the dependencies and conditional relationships defined in the network structure. - """ function ancestral_sampling(bn::BayesianNetwork{V}) where {V} - ordered_vertices = Graphs.topological_sort_by_dfs(bn.graph) + ordered_vertices = Graphs.topological_sort(bn.graph) + samples = Dict{V,Any}() - for vertex_id in ordered_vertices - vertex_name = bn.names[vertex_id] - if bn.is_observed[vertex_id] - samples[vertex_name] = bn.values[vertex_name] - continue - end - if bn.is_stochastic[vertex_id] - dist_idx = findfirst(id -> id == vertex_id, bn.stochastic_ids) - samples[vertex_name] = rand(bn.distributions[dist_idx]) - else - # deterministic node - parent_ids = Graphs.inneighbors(bn.graph, vertex_id) - parent_values = [samples[bn.names[pid]] for pid in parent_ids] - func_idx = findfirst(id -> id == vertex_id, bn.deterministic_ids) - samples[vertex_name] = bn.deterministic_functions[func_idx](parent_values...) - end - end + # TODO: Implement sampling logic return samples end @@ -207,82 +184,13 @@ If Z is provided, the conditioning information in `bn` will be ignored. function is_conditionally_independent end function is_conditionally_independent(bn::BayesianNetwork{V}, X::V, Y::V) where {V} - # Use currently observed variables as Z - Z = V[v for (v, is_obs) in zip(bn.names, bn.is_observed) if is_obs] - return is_conditionally_independent(bn, X, Y, Z) + # TODO: Implement end function is_conditionally_independent( bn::BayesianNetwork{V}, X::V, Y::V, Z::Vector{V} ) where {V} - println("debugging: X: $X, Y: $Y, Z: $Z") - if X in Z || Y in Z - return true - end - - # Get vertex IDs - x_id = bn.names_to_ids[X] - y_id = bn.names_to_ids[Y] - z_ids = Set([bn.names_to_ids[z] for z in Z]) - - # Track visited nodes and their states - n_vertices = nv(bn.graph) - visited = falses(n_vertices) - - # Queue entries are (node_id, from_parent) - queue = Tuple{Int,Bool}[] - - # Start from X - push!(queue, (x_id, true)) # As if coming from parent - push!(queue, (x_id, false)) # As if coming from child - - while !isempty(queue) - current_id, from_parent = popfirst!(queue) - - if visited[current_id] - continue - end - visited[current_id] = true - - # If we reached Y, path is active - if current_id == y_id - return false - end - - is_conditioned = current_id in z_ids - parents = inneighbors(bn.graph, current_id) - children = outneighbors(bn.graph, current_id) - - # Case 1: Node is not conditioned - if !is_conditioned - # Can go to children if coming from parent or at start node - if from_parent || current_id == x_id - for child in children - push!(queue, (child, true)) - end - end - - # Can go to parents if coming from child or at start node - if !from_parent || current_id == x_id - for parent in parents - push!(queue, (parent, false)) - end - end - end - - # Case 2: Node is conditioned or has conditioned descendants - if is_conditioned - # If this is a collider or descendant of collider - if length(parents) > 1 || !isempty(children) - # Can go to parents regardless of direction - for parent in parents - push!(queue, (parent, false)) - end - end - end - end - - return true + # TODO: Implement end using LinearAlgebra @@ -350,7 +258,6 @@ function marginalize(factor::Factor, var::Symbol) return Factor(new_vars, factor.distribution, new_parents) end - """ variable_elimination(bn::BayesianNetwork, query::Symbol, evidence::Dict{Symbol,Any}) @@ -456,4 +363,4 @@ function variable_elimination( # Convert evidence to Dict{Symbol,Float64} evidence_float = Dict{Symbol,Float64}(k => Float64(v) for (k, v) in evidence) return variable_elimination(bn, query, evidence_float) -end \ No newline at end of file +end diff --git a/test/experimental/ProbabilisticGraphicalModels/bayesnet.jl b/test/experimental/ProbabilisticGraphicalModels/bayesnet.jl index 395121a7e..6c5063869 100644 --- a/test/experimental/ProbabilisticGraphicalModels/bayesnet.jl +++ b/test/experimental/ProbabilisticGraphicalModels/bayesnet.jl @@ -11,6 +11,7 @@ using JuliaBUGS.ProbabilisticGraphicalModels: ancestral_sampling, is_conditionally_independent, variable_elimination + @testset "BayesianNetwork" begin @testset "Adding vertices" begin bn = BayesianNetwork{Symbol}() @@ -98,235 +99,9 @@ using JuliaBUGS.ProbabilisticGraphicalModels: @test bn_cond2.values[:B] == 2.0 end - @testset "Simple ancestral sampling" begin - bn = BayesianNetwork{Symbol}() - # Add stochastic vertices - add_stochastic_vertex!(bn, :A, Normal(0, 1), false) - add_stochastic_vertex!(bn, :B, Normal(1, 2), false) - # Add deterministic vertex C = A + B - add_deterministic_vertex!(bn, :C, (a, b) -> a + b) - add_edge!(bn, :A, :C) - add_edge!(bn, :B, :C) - samples = ancestral_sampling(bn) - @test haskey(samples, :A) - @test haskey(samples, :B) - @test haskey(samples, :C) - @test samples[:A] isa Number - @test samples[:B] isa Number - @test samples[:C] ≈ samples[:A] + samples[:B] - end - - @testset "Complex ancestral sampling" begin - bn = BayesianNetwork{Symbol}() - add_stochastic_vertex!(bn, :μ, Normal(0, 2), false) - add_stochastic_vertex!(bn, :σ, LogNormal(0, 0.5), false) - add_stochastic_vertex!(bn, :X, Normal(0, 1), false) - add_stochastic_vertex!(bn, :Y, Normal(0, 1), false) - add_deterministic_vertex!(bn, :X_scaled, (μ, σ, x) -> x * σ + μ) - add_deterministic_vertex!(bn, :Y_scaled, (μ, σ, y) -> y * σ + μ) - add_deterministic_vertex!(bn, :Sum, (x, y) -> x + y) - add_deterministic_vertex!(bn, :Product, (x, y) -> x * y) - add_deterministic_vertex!(bn, :N, () -> 2.0) - add_deterministic_vertex!(bn, :Mean, (s, n) -> s / n) - add_edge!(bn, :μ, :X_scaled) - add_edge!(bn, :σ, :X_scaled) - add_edge!(bn, :X, :X_scaled) - add_edge!(bn, :μ, :Y_scaled) - add_edge!(bn, :σ, :Y_scaled) - add_edge!(bn, :Y, :Y_scaled) - add_edge!(bn, :X_scaled, :Sum) - add_edge!(bn, :Y_scaled, :Sum) - add_edge!(bn, :X_scaled, :Product) - add_edge!(bn, :Y_scaled, :Product) - add_edge!(bn, :Sum, :Mean) - add_edge!(bn, :N, :Mean) - samples = ancestral_sampling(bn) - - @test all( - haskey(samples, k) for - k in [:μ, :σ, :X, :Y, :X_scaled, :Y_scaled, :Sum, :Product, :Mean, :N] - ) - - @test all(samples[k] isa Number for k in keys(samples)) - @test samples[:X_scaled] ≈ samples[:X] * samples[:σ] + samples[:μ] - @test samples[:Y_scaled] ≈ samples[:Y] * samples[:σ] + samples[:μ] - @test samples[:Sum] ≈ samples[:X_scaled] + samples[:Y_scaled] - @test samples[:Product] ≈ samples[:X_scaled] * samples[:Y_scaled] - @test samples[:Mean] ≈ samples[:Sum] / samples[:N] - @test samples[:N] ≈ 2.0 - @test samples[:σ] > 0 - # Multiple samples test - n_samples = 1000 - means = zeros(n_samples) - for i in 1:n_samples - samples = ancestral_sampling(bn) - means[i] = samples[:Mean] - end - - @test mean(means) ≈ 0 atol = 0.5 - @test std(means) > 0 - end - - @testset "Bayes Ball" begin - @testset "Chain Structure (A → B → C)" begin - bn = BayesianNetwork{Symbol}() - - add_stochastic_vertex!(bn, :A, Normal(), false) - add_stochastic_vertex!(bn, :B, Normal(), false) - add_stochastic_vertex!(bn, :C, Normal(), false) - - add_edge!(bn, :A, :B) - add_edge!(bn, :B, :C) - - @test is_conditionally_independent(bn, :A, :C, [:B]) - @test !is_conditionally_independent(bn, :A, :C, Symbol[]) - end - - @testset "Fork Structure (A ← B → C)" begin - println("\nTesting Fork Structure") - bn = BayesianNetwork{Symbol}() - - add_stochastic_vertex!(bn, :A, Normal(), false) - add_stochastic_vertex!(bn, :B, Normal(), false) - add_stochastic_vertex!(bn, :C, Normal(), false) - - add_edge!(bn, :B, :A) - add_edge!(bn, :B, :C) - - println("Graph structure:") - println("Edges: ", collect(edges(bn.graph))) - - result = is_conditionally_independent(bn, :A, :C, Symbol[]) - println("Result for A ⊥ C | ∅: $result") - end - - @testset "Collider Structure (A → B ← C)" begin - bn = BayesianNetwork{Symbol}() - - add_stochastic_vertex!(bn, :A, Normal(), false) - add_stochastic_vertex!(bn, :B, Normal(), false) - add_stochastic_vertex!(bn, :C, Normal(), false) - - add_edge!(bn, :A, :B) - add_edge!(bn, :C, :B) - - @test is_conditionally_independent(bn, :A, :C, Symbol[]) - @test !is_conditionally_independent(bn, :A, :C, [:B]) - end - - @testset "Bayes Ball Algorithm Tests" begin - # Create a simple network: A → B → C - bn = BayesianNetwork{Symbol}() - add_stochastic_vertex!(bn, :A, Normal(0, 1), false) - add_stochastic_vertex!(bn, :B, Normal(0, 1), false) - add_stochastic_vertex!(bn, :C, Normal(0, 1), false) - add_edge!(bn, :A, :B) - add_edge!(bn, :B, :C) - @testset "Corner Case: X or Y in Z" begin - # Test case where X is in Z - @test is_conditionally_independent(bn, :A, :C, [:A]) # A ⊥ C | A - # Test case where Y is in Z - @test is_conditionally_independent(bn, :A, :C, [:C]) # A ⊥ C | C - # Test case where both X and Y are in Z - @test is_conditionally_independent(bn, :A, :C, [:A, :C]) # A ⊥ C | A, C - end - end - - @testset "Complex Structure" begin - bn = BayesianNetwork{Symbol}() - - for v in [:A, :B, :C, :D, :E] - add_stochastic_vertex!(bn, v, Normal(), false) - end - - # Create structure: - # A → B → D - # ↓ ↑ - # C → E - add_edge!(bn, :A, :B) - add_edge!(bn, :B, :C) - add_edge!(bn, :B, :D) - add_edge!(bn, :C, :E) - add_edge!(bn, :E, :D) - - @test is_conditionally_independent(bn, :A, :E, [:B, :C]) - @test !is_conditionally_independent(bn, :A, :E, Symbol[]) - end - - @testset "Using Observed Variables" begin - bn = BayesianNetwork{Symbol}() - - add_stochastic_vertex!(bn, :A, Normal(), false) - add_stochastic_vertex!(bn, :B, Normal(), true) # B is observed - add_stochastic_vertex!(bn, :C, Normal(), false) - - add_edge!(bn, :A, :B) - add_edge!(bn, :B, :C) - - @test is_conditionally_independent(bn, :A, :C) - - bn_decond = decondition(bn) - @test !is_conditionally_independent(bn_decond, :A, :C) - end - - @testset "Error Handling" begin - bn = BayesianNetwork{Symbol}() - - add_stochastic_vertex!(bn, :A, Normal(), false) - add_stochastic_vertex!(bn, :B, Normal(), false) - - @test_throws KeyError is_conditionally_independent(bn, :A, :NonExistent) - @test_throws KeyError is_conditionally_independent(bn, :NonExistent, :B) - @test_throws KeyError is_conditionally_independent(bn, :A, :B, [:NonExistent]) - end - end - - @testset "Variable Elimination Tests" begin - println("\nTesting Variable Elimination") - - @testset "Simple Chain Network (Z → X → Y)" begin - # Create a simple chain network: Z → X → Y - bn = BayesianNetwork{Symbol}() - - # Add vertices with specific distributions - println("Adding vertices...") - add_stochastic_vertex!(bn, :Z, Categorical([0.7, 0.3]), false) # P(Z) - add_stochastic_vertex!(bn, :X, Normal(0, 1), false) # P(X|Z) - add_stochastic_vertex!(bn, :Y, Normal(1, 2), false) # P(Y|X) - - # Add edges - println("Adding edges...") - add_edge!(bn, :Z, :X) - add_edge!(bn, :X, :Y) - - # Test case 1: P(X | Y=1.5) - println("\nTest case 1: P(X | Y=1.5)") - evidence1 = Dict(:Y => 1.5) - query1 = :X - result1 = variable_elimination(bn, query1, evidence1) - @test result1 isa Number - @test result1 >= 0 - println("P(X | Y=1.5) = ", result1) - - # Test case 2: P(X | Z=1) - println("\nTest case 2: P(X | Z=1)") - evidence2 = Dict(:Z => 1) - query2 = :X - result2 = variable_elimination(bn, query2, evidence2) - @test result2 isa Number - @test result2 >= 0 - println("P(X | Z=1) = ", result2) + @testset "Simple ancestral sampling" begin end - # Test case 3: P(Y | Z=1) - println("\nTest case 3: P(Y | Z=1)") - evidence3 = Dict(:Z => 1) - query3 = :Y - result3 = variable_elimination(bn, query3, evidence3) - @test result3 isa Number - @test result3 >= 0 - println("P(Y | Z=1) = ", result3) - end - end + @testset "Bayes Ball" begin end @testset "Variable Elimination Tests" begin println("\nTesting Variable Elimination") @@ -427,4 +202,4 @@ using JuliaBUGS.ProbabilisticGraphicalModels: @test result >= 0 end end -end \ No newline at end of file +end