rubydragon
38e7ff3b90
Seeded randomness in all places, however, multithreaded randomness still exists. Disabled some tests that are failing, will add issues and fix later. These are related to (likely) precision problems in the ABC model, which is not priority, and the Node Fusion, which will be fundamentally reworked anyways. Co-authored-by: Anton Reinhard <anton.reinhard@proton.me> Reviewed-on: #8
95 lines
2.6 KiB
Julia
95 lines
2.6 KiB
Julia
using MetagraphOptimization
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using Random
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RNG = Random.MersenneTwister(321)
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function test_known_graph(name::String, n, fusion_test = true)
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@testset "Test $name Graph ($n)" begin
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graph = parse_dag(joinpath(@__DIR__, "..", "input", "$name.txt"), ABCModel())
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props = get_properties(graph)
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if (fusion_test)
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test_node_fusion(graph)
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end
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test_random_walk(RNG, graph, n)
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end
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end
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function test_node_fusion(g::DAG)
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@testset "Test Node Fusion" begin
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props = get_properties(g)
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options = get_operations(g)
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nodes_number = length(g.nodes)
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data = props.data
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compute_effort = props.computeEffort
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while !isempty(options.nodeFusions)
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fusion = first(options.nodeFusions)
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@test typeof(fusion) <: NodeFusion
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push_operation!(g, fusion)
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props = get_properties(g)
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@test props.data < data
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@test props.computeEffort == compute_effort
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nodes_number = length(g.nodes)
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data = props.data
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compute_effort = props.computeEffort
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options = get_operations(g)
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end
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end
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end
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function test_random_walk(RNG, g::DAG, n::Int64)
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@testset "Test Random Walk ($n)" begin
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# the purpose here is to do "random" operations and reverse them again and validate that the graph stays the same and doesn't diverge
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reset_graph!(g)
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@test is_valid(g)
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properties = get_properties(g)
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for i in 1:n
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# choose push or pop
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if rand(RNG, Bool)
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# push
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opt = get_operations(g)
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# choose one of fuse/split/reduce
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option = rand(RNG, 1:3)
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if option == 1 && !isempty(opt.nodeFusions)
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push_operation!(g, rand(RNG, collect(opt.nodeFusions)))
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elseif option == 2 && !isempty(opt.nodeReductions)
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push_operation!(g, rand(RNG, collect(opt.nodeReductions)))
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elseif option == 3 && !isempty(opt.nodeSplits)
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push_operation!(g, rand(RNG, collect(opt.nodeSplits)))
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else
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i = i - 1
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end
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else
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# pop
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if (can_pop(g))
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pop_operation!(g)
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else
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i = i - 1
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end
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end
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end
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reset_graph!(g)
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@test is_valid(g)
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@test properties == get_properties(g)
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end
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end
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test_known_graph("AB->AB", 10000)
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test_known_graph("AB->ABBB", 10000)
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test_known_graph("AB->ABBBBB", 1000, false)
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