From 9a4ec5c124048c2caf70eced5fc5fcd4eb635efe Mon Sep 17 00:00:00 2001
From: etienneINSA <etienne.mace_de_gastines@insa-rouen.fr>
Date: Thu, 2 May 2024 16:28:48 +0200
Subject: [PATCH] add accessors
---
src/GraphsMatching.jl | 50 +++-
test/runtests.jl | 535 +++++++++++++++++++++++-------------------
2 files changed, 338 insertions(+), 247 deletions(-)
diff --git a/src/GraphsMatching.jl b/src/GraphsMatching.jl
index 81e35df..f0e261f 100644
--- a/src/GraphsMatching.jl
+++ b/src/GraphsMatching.jl
@@ -7,10 +7,20 @@ using SparseArrays: spzeros
using JuMP
using MathOptInterface
const MOI = MathOptInterface
-import BlossomV # 'using BlossomV' leads to naming conflicts with JuMP
+using BlossomV: BlossomV # 'using BlossomV' leads to naming conflicts with JuMP
using Hungarian
-export MatchingResult, maximum_weight_matching, maximum_weight_maximal_matching, minimum_weight_perfect_matching, HungarianAlgorithm, LPAlgorithm
+export MatchingResult,
+ weight,
+ is_matched_vertex,
+ matching_vertex,
+ matching_vertices,
+ matched_edges,
+ maximum_weight_matching,
+ maximum_weight_maximal_matching,
+ minimum_weight_perfect_matching,
+ HungarianAlgorithm,
+ LPAlgorithm
"""
struct MatchingResult{U}
@@ -30,6 +40,42 @@ struct MatchingResult{U<:Real}
mate::Vector{Int}
end
+"""
+ weight(m::MatchingResult)
+
+ returns the total weight of the matching
+"""
+weight(m::MatchingResult) = m.weight
+
+"""
+ is_matched_vertex(m::MatchingResult, u)
+
+ returns true if vertex `u` is matched with another vertex
+"""
+is_matched_vertex(m::MatchingResult, u) = (m.mate[u] != -1)
+
+"""
+ matching_vertex(m::MatchingResult, u)
+
+ returns the vertex matched with `u` (-1 if `u` is not matched)
+"""
+matching_vertex(m::MatchingResult, u) = m.mate[u]
+
+"""
+ matching_vertices(m::MatchingResult)
+
+ returns a list of the matching vertices (-1 if the vertex is not matched)
+"""
+matching_vertices(m::MatchingResult) = m.mate
+
+"""
+ matching_vertex(m::MatchingResult, u)
+
+ returns a list of the matched edges
+"""
+matched_edges(m::MatchingResult) =
+ [Edge(u, v) for (u, v) in enumerate(matching_vertices(m)) if (u < v)]
+
include("lp.jl")
include("maximum_weight_matching.jl")
include("blossomv.jl")
diff --git a/test/runtests.jl b/test/runtests.jl
index 0beb14f..db53099 100644
--- a/test/runtests.jl
+++ b/test/runtests.jl
@@ -6,255 +6,300 @@ using JuMP
using LinearAlgebra: I
@testset "GraphsMatching" begin
+ @testset "accessors" begin
+ match = MatchingResult(45, [4, -1, 7, 1, 6, 5, 3, -1])
-@testset "maximum_weight_matching" begin
- g = complete_graph(3)
- w = [
- 1 2 1
- 1 1 1
- 3 1 1
- ]
- match = maximum_weight_matching(g, optimizer_with_attributes(Cbc.Optimizer, "LogLevel" => 0), w)
- @test match.mate[1] == 3
- @test match.weight ≈ 3
-
- g = complete_graph(3)
- w = zeros(3,3)
- w[1,2] = 1
- w[3,2] = 1
- w[1,3] = 1
- match = maximum_weight_matching(g, optimizer_with_attributes(Cbc.Optimizer, "LogLevel" => 0),w)
- @test match.weight ≈ 1
-
-
- g = Graph(4)
- add_edge!(g, 1,3)
- add_edge!(g, 1,4)
- add_edge!(g, 2,4)
-
- w =zeros(4,4)
- w[1,3] = 1
- w[1,4] = 3
- w[2,4] = 1
-
- match = maximum_weight_matching(g, optimizer_with_attributes(Cbc.Optimizer, "LogLevel" => 0),w)
- @test match.weight ≈ 3
- @test match.mate[1] == 4
- @test match.mate[2] == -1
- @test match.mate[3] == -1
- @test match.mate[4] == 1
-
- g = Graph(4)
- add_edge!(g, 1,2)
- add_edge!(g, 2,3)
- add_edge!(g, 3,1)
- add_edge!(g, 3,4)
- match = maximum_weight_matching(g, optimizer_with_attributes(Cbc.Optimizer, "LogLevel" => 0))
- @test match.weight ≈ 2
- @test match.mate[1] == 2
- @test match.mate[2] == 1
- @test match.mate[3] == 4
- @test match.mate[4] == 3
-
- w = zeros(4,4)
- w[1,2] = 1
- w[2,3] = 1
- w[1,3] = 1
- w[3,4] = 1
-
- match = maximum_weight_matching(g, optimizer_with_attributes(Cbc.Optimizer, "LogLevel" => 0), w)
- @test match.weight ≈ 2
- @test match.mate[1] == 2
- @test match.mate[2] == 1
- @test match.mate[3] == 4
- @test match.mate[4] == 3
-
- w = zeros(4,4)
- w[1,2] = 1
- w[2,3] = 1
- w[1,3] = 5
- w[3,4] = 1
-
- match = maximum_weight_matching(g, optimizer_with_attributes(Cbc.Optimizer, "LogLevel" => 0),w)
- @test match.weight ≈ 5
- @test match.mate[1] == 3
- @test match.mate[2] == -1
- @test match.mate[3] == 1
- @test match.mate[4] == -1
-end
-
-
-@testset "maximum_weight_maximal_matching" begin
-
- g = complete_bipartite_graph(2,2)
- w = zeros(4,4)
- w[1,3] = 10.
- w[1,4] = 1.
- w[2,3] = 2.
- w[2,4] = 11.
- match = maximum_weight_maximal_matching(g, w, algorithm=LPAlgorithm(), optimizer=optimizer_with_attributes(Cbc.Optimizer, "LogLevel" => 0))
- @test match.weight ≈ 21
- @test match.mate[1] == 3
- @test match.mate[3] == 1
- @test match.mate[2] == 4
- @test match.mate[4] == 2
-
- g =complete_bipartite_graph(2,4)
- w =zeros(6,6)
- w[1,3] = 10
- w[1,4] = 0.5
- w[2,3] = 11
- w[2,4] = 1
- match = maximum_weight_maximal_matching(g, w, algorithm=LPAlgorithm(), optimizer=optimizer_with_attributes(Cbc.Optimizer, "LogLevel" => 0))
- @test match.weight ≈ 11.5
- @test match.mate[1] == 4
- @test match.mate[4] == 1
- @test match.mate[2] == 3
- @test match.mate[3] == 2
-
- g =complete_bipartite_graph(2,6)
- w =zeros(8,8)
- w[1,3] = 10
- w[1,4] = 0.5
- w[2,3] = 11
- w[2,4] = 1
- w[2,5] = -1
- w[2,6] = -1
- match = maximum_weight_maximal_matching(g, w, algorithm=LPAlgorithm(), optimizer=optimizer_with_attributes(Cbc.Optimizer, "LogLevel" => 0), cutoff=0)
- @test match.weight ≈ 11.5
- @test match.mate[1] == 4
- @test match.mate[4] == 1
- @test match.mate[2] == 3
- @test match.mate[3] == 2
-
- g =complete_bipartite_graph(4,2)
- w = zeros(6,6)
- w[3,5] = 10
- w[3,6] = 0.5
- w[2,5] = 11
- w[1,6] = 1
- w[1,5] = -1
-
- match = maximum_weight_maximal_matching(g, w, algorithm=LPAlgorithm(), optimizer=optimizer_with_attributes(Cbc.Optimizer, "LogLevel" => 0), cutoff=0)
- @test match.weight ≈ 12
- @test match.mate[1] == 6
- @test match.mate[2] == 5
- @test match.mate[3] == -1
- @test match.mate[4] == -1
- @test match.mate[5] == 2
- @test match.mate[6] == 1
-
-
- g = complete_bipartite_graph(2, 2)
- w = zeros(4, 4)
- w[1, 3] = 10.
- w[1, 4] = 1.
- w[2, 3] = 2.
- w[2, 4] = 11.
- match = maximum_weight_maximal_matching(g, w, algorithm=HungarianAlgorithm())
- @test match.weight ≈ 21
- @test match.mate[1] == 3
- @test match.mate[3] == 1
- @test match.mate[2] == 4
- @test match.mate[4] == 2
-
- g = complete_graph(3)
- w = zeros(3, 3)
- @test ! is_bipartite(g)
- @test_throws ErrorException maximum_weight_maximal_matching(g, w, algorithm=HungarianAlgorithm())
-
- g = complete_bipartite_graph(2, 4)
- w = zeros(6, 6)
- w[1, 3] = 10
- w[1, 4] = 0.5
- w[2, 3] = 11
- w[2, 4] = 1
- match = maximum_weight_maximal_matching(g, w, algorithm=HungarianAlgorithm())
- @test match.weight ≈ 11.5
-
- g = Graph(4)
- add_edge!(g, 1, 3)
- add_edge!(g, 1, 4)
- add_edge!(g, 2, 4)
- w = zeros(4, 4)
- w[1, 3] = 1
- w[1, 4] = 3
- w[2, 4] = 1
- match = maximum_weight_maximal_matching(g, w, algorithm=HungarianAlgorithm())
- @test match.weight ≈ 2
-
-end
+ @test weight(match) == 45
+ @test is_matched_vertex(match, 3) == true
+ @test is_matched_vertex(match, 2) == false
+ @test matching_vertex(match, 5) == 6
+ @test matching_vertex(match, 8) == -1
-@testset "minimum_weight_perfect_matching" begin
-
- w = Dict(Edge(1,2)=> 500)
- g =Graph(2)
- add_edge!(g,1,2)
- match = minimum_weight_perfect_matching(g, w)
- @test match.mate[1] == 2
-
-
- w=Dict( Edge(1,2)=>500,
- Edge(1,3)=>600,
- Edge(2,3)=>700,
- Edge(3,4)=>100,
- Edge(2,4)=>1000)
-
- g = complete_graph(4)
- match = minimum_weight_perfect_matching(g, w)
- @test match.mate[1] == 2
- @test match.mate[2] == 1
- @test match.mate[3] == 4
- @test match.mate[4] == 3
- @test match.weight ≈ 600
-
- w = Dict(
- Edge(1, 2) => 500,
- Edge(1, 3) => 400,
- Edge(2, 3) => 300,
- Edge(3, 4) => 1000,
- Edge(2, 4) => 1000
- )
- g = complete_graph(4)
- match = minimum_weight_perfect_matching(g, w)
- @test match.mate[1] == 3
- @test match.mate[2] == 4
- @test match.mate[3] == 1
- @test match.mate[4] == 2
- @test match.weight ≈ 1400
-
- g =complete_bipartite_graph(2,2)
- w =Dict{Edge,Float64}()
- w[Edge(1,3)] = -10
- w[Edge(1,4)] = -0.5
- w[Edge(2,3)] = -11
- w[Edge(2,4)] = -1
-
- match = minimum_weight_perfect_matching(g, w)
- @test match.mate[1] == 4
- @test match.mate[4] == 1
- @test match.mate[2] == 3
- @test match.mate[3] == 2
- @test match.weight ≈ -11.5
-
-
- g = complete_graph(4)
- w = Dict{Edge,Float64}()
- w[Edge(1,3)] = 10
- w[Edge(1,4)] = 0.5
- w[Edge(2,3)] = 11
- w[Edge(2,4)] = 2
- w[Edge(1,2)] = 100
-
- match = minimum_weight_perfect_matching(g, w, 50)
- @test match.mate[1] == 4
- @test match.mate[4] == 1
- @test match.mate[2] == 3
- @test match.mate[3] == 2
- @test match.weight ≈ 11.5
-end
+ @test matching_vertices(match) == [4, -1, 7, 1, 6, 5, 3, -1]
+ m_edges = matched_edges(match)
+ @test length(m_edges) == 3
+ @test Edge(1, 4) ∈ m_edges
+ @test Edge(3, 7) ∈ m_edges
+ @test Edge(5, 6) ∈ m_edges
+ end
+ @testset "maximum_weight_matching" begin
+ g = complete_graph(3)
+ w = [
+ 1 2 1
+ 1 1 1
+ 3 1 1
+ ]
+ match = maximum_weight_matching(
+ g, optimizer_with_attributes(Cbc.Optimizer, "LogLevel" => 0), w
+ )
+ @test matching_vertex(match, 1) == 3
+ @test weight(match) ≈ 3
+
+ g = complete_graph(3)
+ w = zeros(3, 3)
+ w[1, 2] = 1
+ w[3, 2] = 1
+ w[1, 3] = 1
+ match = maximum_weight_matching(
+ g, optimizer_with_attributes(Cbc.Optimizer, "LogLevel" => 0), w
+ )
+ @test weight(match) ≈ 1
+
+ g = Graph(4)
+ add_edge!(g, 1, 3)
+ add_edge!(g, 1, 4)
+ add_edge!(g, 2, 4)
+
+ w = zeros(4, 4)
+ w[1, 3] = 1
+ w[1, 4] = 3
+ w[2, 4] = 1
+
+ match = maximum_weight_matching(
+ g, optimizer_with_attributes(Cbc.Optimizer, "LogLevel" => 0), w
+ )
+ @test weight(match) ≈ 3
+ @test matching_vertex(match, 1) == 4
+ @test matching_vertex(match, 2) == -1
+ @test matching_vertex(match, 3) == -1
+ @test matching_vertex(match, 4) == 1
+
+ g = Graph(4)
+ add_edge!(g, 1, 2)
+ add_edge!(g, 2, 3)
+ add_edge!(g, 3, 1)
+ add_edge!(g, 3, 4)
+ match = maximum_weight_matching(
+ g, optimizer_with_attributes(Cbc.Optimizer, "LogLevel" => 0)
+ )
+ @test weight(match) ≈ 2
+ @test matching_vertex(match, 1) == 2
+ @test matching_vertex(match, 2) == 1
+ @test matching_vertex(match, 3) == 4
+ @test matching_vertex(match, 4) == 3
+
+ w = zeros(4, 4)
+ w[1, 2] = 1
+ w[2, 3] = 1
+ w[1, 3] = 1
+ w[3, 4] = 1
+
+ match = maximum_weight_matching(
+ g, optimizer_with_attributes(Cbc.Optimizer, "LogLevel" => 0), w
+ )
+ @test weight(match) ≈ 2
+ @test matching_vertex(match, 1) == 2
+ @test matching_vertex(match, 2) == 1
+ @test matching_vertex(match, 3) == 4
+ @test matching_vertex(match, 4) == 3
+
+ w = zeros(4, 4)
+ w[1, 2] = 1
+ w[2, 3] = 1
+ w[1, 3] = 5
+ w[3, 4] = 1
+
+ match = maximum_weight_matching(
+ g, optimizer_with_attributes(Cbc.Optimizer, "LogLevel" => 0), w
+ )
+ @test weight(match) ≈ 5
+ @test matching_vertex(match, 1) == 3
+ @test matching_vertex(match, 2) == -1
+ @test matching_vertex(match, 3) == 1
+ @test matching_vertex(match, 4) == -1
+ end
+
+ @testset "maximum_weight_maximal_matching" begin
+ g = complete_bipartite_graph(2, 2)
+ w = zeros(4, 4)
+ w[1, 3] = 10.0
+ w[1, 4] = 1.0
+ w[2, 3] = 2.0
+ w[2, 4] = 11.0
+ match = maximum_weight_maximal_matching(
+ g,
+ w;
+ algorithm=LPAlgorithm(),
+ optimizer=optimizer_with_attributes(Cbc.Optimizer, "LogLevel" => 0),
+ )
+ @test weight(match) ≈ 21
+ @test matching_vertex(match, 1) == 3
+ @test matching_vertex(match, 3) == 1
+ @test matching_vertex(match, 2) == 4
+ @test matching_vertex(match, 4) == 2
+
+ g = complete_bipartite_graph(2, 4)
+ w = zeros(6, 6)
+ w[1, 3] = 10
+ w[1, 4] = 0.5
+ w[2, 3] = 11
+ w[2, 4] = 1
+ match = maximum_weight_maximal_matching(
+ g,
+ w;
+ algorithm=LPAlgorithm(),
+ optimizer=optimizer_with_attributes(Cbc.Optimizer, "LogLevel" => 0),
+ )
+ @test weight(match) ≈ 11.5
+ @test matching_vertex(match, 1) == 4
+ @test matching_vertex(match, 4) == 1
+ @test matching_vertex(match, 2) == 3
+ @test matching_vertex(match, 3) == 2
+
+ g = complete_bipartite_graph(2, 6)
+ w = zeros(8, 8)
+ w[1, 3] = 10
+ w[1, 4] = 0.5
+ w[2, 3] = 11
+ w[2, 4] = 1
+ w[2, 5] = -1
+ w[2, 6] = -1
+ match = maximum_weight_maximal_matching(
+ g,
+ w;
+ algorithm=LPAlgorithm(),
+ optimizer=optimizer_with_attributes(Cbc.Optimizer, "LogLevel" => 0),
+ cutoff=0,
+ )
+ @test weight(match) ≈ 11.5
+ @test matching_vertex(match, 1) == 4
+ @test matching_vertex(match, 4) == 1
+ @test matching_vertex(match, 2) == 3
+ @test matching_vertex(match, 3) == 2
+
+ g = complete_bipartite_graph(4, 2)
+ w = zeros(6, 6)
+ w[3, 5] = 10
+ w[3, 6] = 0.5
+ w[2, 5] = 11
+ w[1, 6] = 1
+ w[1, 5] = -1
+
+ match = maximum_weight_maximal_matching(
+ g,
+ w;
+ algorithm=LPAlgorithm(),
+ optimizer=optimizer_with_attributes(Cbc.Optimizer, "LogLevel" => 0),
+ cutoff=0,
+ )
+ @test weight(match) ≈ 12
+ @test matching_vertex(match, 1) == 6
+ @test matching_vertex(match, 2) == 5
+ @test matching_vertex(match, 3) == -1
+ @test matching_vertex(match, 4) == -1
+ @test matching_vertex(match, 5) == 2
+ @test matching_vertex(match, 6) == 1
+
+ g = complete_bipartite_graph(2, 2)
+ w = zeros(4, 4)
+ w[1, 3] = 10.0
+ w[1, 4] = 1.0
+ w[2, 3] = 2.0
+ w[2, 4] = 11.0
+ match = maximum_weight_maximal_matching(g, w; algorithm=HungarianAlgorithm())
+ @test weight(match) ≈ 21
+ @test matching_vertex(match, 1) == 3
+ @test matching_vertex(match, 3) == 1
+ @test matching_vertex(match, 2) == 4
+ @test matching_vertex(match, 4) == 2
+
+ g = complete_graph(3)
+ w = zeros(3, 3)
+ @test !is_bipartite(g)
+ @test_throws ErrorException maximum_weight_maximal_matching(
+ g, w, algorithm=HungarianAlgorithm()
+ )
+
+ g = complete_bipartite_graph(2, 4)
+ w = zeros(6, 6)
+ w[1, 3] = 10
+ w[1, 4] = 0.5
+ w[2, 3] = 11
+ w[2, 4] = 1
+ match = maximum_weight_maximal_matching(g, w; algorithm=HungarianAlgorithm())
+ @test weight(match) ≈ 11.5
+
+ g = Graph(4)
+ add_edge!(g, 1, 3)
+ add_edge!(g, 1, 4)
+ add_edge!(g, 2, 4)
+ w = zeros(4, 4)
+ w[1, 3] = 1
+ w[1, 4] = 3
+ w[2, 4] = 1
+ match = maximum_weight_maximal_matching(g, w; algorithm=HungarianAlgorithm())
+ @test weight(match) ≈ 2
+ end
+
+ @testset "minimum_weight_perfect_matching" begin
+ w = Dict(Edge(1, 2) => 500)
+ g = Graph(2)
+ add_edge!(g, 1, 2)
+ match = minimum_weight_perfect_matching(g, w)
+ @test matching_vertex(match, 1) == 2
+
+ w = Dict(
+ Edge(1, 2) => 500,
+ Edge(1, 3) => 600,
+ Edge(2, 3) => 700,
+ Edge(3, 4) => 100,
+ Edge(2, 4) => 1000,
+ )
+
+ g = complete_graph(4)
+ match = minimum_weight_perfect_matching(g, w)
+ @test matching_vertex(match, 1) == 2
+ @test matching_vertex(match, 2) == 1
+ @test matching_vertex(match, 3) == 4
+ @test matching_vertex(match, 4) == 3
+ @test weight(match) ≈ 600
+
+ w = Dict(
+ Edge(1, 2) => 500,
+ Edge(1, 3) => 400,
+ Edge(2, 3) => 300,
+ Edge(3, 4) => 1000,
+ Edge(2, 4) => 1000,
+ )
+ g = complete_graph(4)
+ match = minimum_weight_perfect_matching(g, w)
+ @test matching_vertex(match, 1) == 3
+ @test matching_vertex(match, 2) == 4
+ @test matching_vertex(match, 3) == 1
+ @test matching_vertex(match, 4) == 2
+ @test weight(match) ≈ 1400
+
+ g = complete_bipartite_graph(2, 2)
+ w = Dict{Edge,Float64}()
+ w[Edge(1, 3)] = -10
+ w[Edge(1, 4)] = -0.5
+ w[Edge(2, 3)] = -11
+ w[Edge(2, 4)] = -1
+
+ match = minimum_weight_perfect_matching(g, w)
+ @test matching_vertex(match, 1) == 4
+ @test matching_vertex(match, 4) == 1
+ @test matching_vertex(match, 2) == 3
+ @test matching_vertex(match, 3) == 2
+ @test weight(match) ≈ -11.5
+
+ g = complete_graph(4)
+ w = Dict{Edge,Float64}()
+ w[Edge(1, 3)] = 10
+ w[Edge(1, 4)] = 0.5
+ w[Edge(2, 3)] = 11
+ w[Edge(2, 4)] = 2
+ w[Edge(1, 2)] = 100
+
+ match = minimum_weight_perfect_matching(g, w, 50)
+ @test matching_vertex(match, 1) == 4
+ @test matching_vertex(match, 4) == 1
+ @test matching_vertex(match, 2) == 3
+ @test matching_vertex(match, 3) == 2
+ @test weight(match) ≈ 11.5
+ end
end