actuarialopensource / benchmarks alternative approaches

aos_benchmark.jl

### A Pluto.jl notebook ###
# v0.19.11

using Markdown
using InteractiveUtils

# ╔═╡ f75704b5-fe24-4a1e-acdb-2e4123ec0906
using MortalityTables

# ╔═╡ dd21605c-6012-401e-a0aa-ed8dc989e00d
using BenchmarkTools

# ╔═╡ 217e4ba8-6d17-445c-8f53-23e53218a1b2
using LifeContingencies, Yields, ActuaryUtilities

# ╔═╡ 276f8f13-f604-49f4-b9a3-a2eca2e7e482
md"## Base benchmark"

# ╔═╡ 1052a958-e92b-462f-9c23-5ab311bbc94b
function runner()
    tbls = [MortalityTables.table(i) for i in 3299:3308]
    issue_ages = 18:50
    durations = 1:25
    timesteps = 0:29
    q = [
        tbl.select[issue_age][issue_age+(duration-1)+timestep]
        for timestep in timesteps, (tbl, issue_age, duration) in vec(collect(Iterators.product(tbls, issue_ages, durations)))
    ]
    npx = [1; cumprod(1 .- q[begin:end-1, :], dims=1)]
    v = 1 / 1.02
    v_eoy = v .^ (1 .+ timesteps) # this broadcasts
    unit_claims_discounted = npx .* q .* v_eoy
    sum(unit_claims_discounted)
end

# ╔═╡ 4d6eb80d-5dac-4f27-8565-666d23aa8e62
@assert runner() == 1904.486552663679

# ╔═╡ a0d6b326-b97f-4380-a48b-2a91c7f1cfb1
b = @benchmark runner()

# ╔═╡ 7e236c52-4e59-46f9-b0ac-1cf4c189dc50
md" ## `for` loops

Adapting the code from the [Life Modeling Problem Benchmarks](https://github.com/JuliaActuary/Learn/tree/master/Benchmarks/LifeModelingProblem)
"

# ╔═╡ 0b6c8dfb-46d2-4f12-b888-4279382d12dd
@inline function npv(qs,r,term=length(qs))
    inforce, result = 1.0, 0.0
    v = 1 / ( 1 + r)
    v_t = v
    @inbounds @simd for t in 1:min(term, length(qs))
        q = qs[t]
        result += inforce * q * v_t
        inforce = inforce*(1 - q)
        v_t *= v
    end
    return result
end

# ╔═╡ 41a11cca-6b2f-48e7-9cb4-c2ea67753347
md" ### `mapreduce` over a `for` loop function"

# ╔═╡ d3e785a9-6fe3-45d6-aa3c-bb71ca351676
function runner4(tbls = [MortalityTables.table(i) for i in 3299:3308])
	issue_ages = 18:50
    durations = 1:25
	term = 29
	function f(inputs) 
		tbl,ia,dur = inputs
		start_age = ia + dur -1 
		@views npv(tbl.select[ia][start_age:start_age+term],0.02)
	end
	mapreduce(f,+,Iterators.product(tbls, issue_ages, durations))
end

# ╔═╡ e39fe81d-95ba-40ad-9790-054f28171f3d
@benchmark runner4()

# ╔═╡ 273ffc02-9e70-432c-bf47-ba3d0de13073
@benchmark runner4(tbls) setup=(tbls=[MortalityTables.table(i) for i in 3299:3308])

# ╔═╡ d76e1c1f-eeba-415d-a3de-2fefda5281e5
md" ### nested `for` loops"

# ╔═╡ dbd80764-ec21-4b24-87f6-827e9f74cc40
function runner_bm2(tbls=[MortalityTables.table(i) for i in 3299:3308])
	issue_ages = 18:50
    durations = 1:25
	term = 29
	total = 0.
	@inbounds for i in eachindex(tbls), ia in issue_ages, dur in durations
		start_age = ia + dur -1 
		total += @views npv(tbls[i].select[ia][start_age:start_age+term],0.02)
	end
	return total
end

# ╔═╡ dc9070a7-c0d2-4336-ab7b-0baca530a0a0
@benchmark runner_bm2()

# ╔═╡ c50775d0-b0e2-48f5-990b-2daa2ce92a77
@benchmark runner_bm2(tbls) setup=(tbls=[MortalityTables.table(i) for i in 3299:3308])

# ╔═╡ 5cf9d5f8-c61b-4477-993a-fa9d6c900169
md" ## More ideomatic JuliaActuary code

Use Yields and LifeContingencies to handle the decrements and discounting.
"

# ╔═╡ b9a3540c-e3d5-4cfc-9136-95da9ff53078
function runner2()
	tbls = [MortalityTables.table(i) for i in 3299:3308]
    issue_ages = 18:50
    durations = 1:25
    timesteps = 0:29
	y = Yields.Constant(0.02)
	
	function make_insurance(inputs)
		tbl,ia,dur = inputs
		lc = LifeContingency(
			SingleLife(mortality = tbl.select[ia][ia+dur-1:end]), # life
			y, # yield
		)
		ins = Insurance(lc,30)
		pv(ins)
	end
	mapreduce(make_insurance,+,Iterators.product(tbls,issue_ages,durations) )
end

# ╔═╡ 86f1225a-523b-4a7b-aad9-f565d4836ed6
md"Same thing, but `@views` avoids copying when slicing array:"

# ╔═╡ 3ebd0b5b-37cd-4ea5-8447-74ae11abc131
function runner3()
	tbls = [MortalityTables.table(i) for i in 3299:3308]
    issue_ages = 18:50
    durations = 1:25
    timesteps = 0:29
	y = Yields.Constant(0.02)
	
	function make_insurance(inputs)
		tbl,ia,dur = inputs
		lc = @views  LifeContingency(
			SingleLife(mortality = tbl.select[ia][ia+dur-1:end]),
			y)
		i = Insurance(lc,30)
		pv(i)
	end
	mapreduce(make_insurance,+,Iterators.product(tbls,issue_ages,durations) )
end

# ╔═╡ 05f5e3c5-e2e4-4bbd-ac7c-84c69eee2f14
@benchmark runner2()

# ╔═╡ 1c9a92c3-69ab-4ecd-a04a-ff5dd3d7807d
@benchmark runner3()

# ╔═╡ 00000000-0000-0000-0000-000000000001
PLUTO_PROJECT_TOML_CONTENTS = """
[deps]
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Yields = "d7e99b2f-e7f3-4d9e-9f01-2338fc023ad3"

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Yields = "~3.3.0"
"""

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uuid = "a4e569a6-e804-4fa4-b0f3-eef7a1d5b13e"
version = "1.10.0"

[[deps.TensorCore]]
deps = ["LinearAlgebra"]
git-tree-sha1 = "1feb45f88d133a655e001435632f019a9a1bcdb6"
uuid = "62fd8b95-f654-4bbd-a8a5-9c27f68ccd50"
version = "0.1.1"

[[deps.Test]]
deps = ["InteractiveUtils", "Logging", "Random", "Serialization"]
uuid = "8dfed614-e22c-5e08-85e1-65c5234f0b40"

[[deps.Transducers]]
deps = ["Adapt", "ArgCheck", "BangBang", "Baselet", "CompositionsBase", "DefineSingletons", "Distributed", "InitialValues", "Logging", "Markdown", "MicroCollections", "Requires", "Setfield", "SplittablesBase", "Tables"]
git-tree-sha1 = "c76399a3bbe6f5a88faa33c8f8a65aa631d95013"
uuid = "28d57a85-8fef-5791-bfe6-a80928e7c999"
version = "0.4.73"

[[deps.UUIDs]]
deps = ["Random", "SHA"]
uuid = "cf7118a7-6976-5b1a-9a39-7adc72f591a4"

[[deps.UnPack]]
git-tree-sha1 = "387c1f73762231e86e0c9c5443ce3b4a0a9a0c2b"
uuid = "3a884ed6-31ef-47d7-9d2a-63182c4928ed"
version = "1.0.2"

[[deps.Unicode]]
uuid = "4ec0a83e-493e-50e2-b9ac-8f72acf5a8f5"

[[deps.UnicodePlots]]
deps = ["ColorTypes", "Contour", "Crayons", "Dates", "FileIO", "FreeTypeAbstraction", "LazyModules", "LinearAlgebra", "MarchingCubes", "NaNMath", "Printf", "SparseArrays", "StaticArrays", "StatsBase", "Unitful"]
git-tree-sha1 = "ae67ab0505b9453655f7d5ea65183a1cd1b3cfa0"
uuid = "b8865327-cd53-5732-bb35-84acbb429228"
version = "2.12.4"

[[deps.Unitful]]
deps = ["ConstructionBase", "Dates", "LinearAlgebra", "Random"]
git-tree-sha1 = "b649200e887a487468b71821e2644382699f1b0f"
uuid = "1986cc42-f94f-5a68-af5c-568840ba703d"
version = "1.11.0"

[[deps.WoodburyMatrices]]
deps = ["LinearAlgebra", "SparseArrays"]
git-tree-sha1 = "de67fa59e33ad156a590055375a30b23c40299d3"
uuid = "efce3f68-66dc-5838-9240-27a6d6f5f9b6"
version = "0.5.5"

[[deps.XML2_jll]]
deps = ["Artifacts", "JLLWrappers", "Libdl", "Libiconv_jll", "Pkg", "Zlib_jll"]
git-tree-sha1 = "58443b63fb7e465a8a7210828c91c08b92132dff"
uuid = "02c8fc9c-b97f-50b9-bbe4-9be30ff0a78a"
version = "2.9.14+0"

[[deps.XMLDict]]
deps = ["EzXML", "IterTools", "OrderedCollections"]
git-tree-sha1 = "d9a3faf078210e477b291c79117676fca54da9dd"
uuid = "228000da-037f-5747-90a9-8195ccbf91a5"
version = "0.4.1"

[[deps.Yields]]
deps = ["BSplineKit", "FinanceCore", "ForwardDiff", "LinearAlgebra", "LsqFit", "Optim", "Reexport", "Roots", "UnicodePlots"]
git-tree-sha1 = "9fb278d913ac38eb62662b525a98d64b4321680c"
uuid = "d7e99b2f-e7f3-4d9e-9f01-2338fc023ad3"
version = "3.3.0"

[[deps.Zlib_jll]]
deps = ["Libdl"]
uuid = "83775a58-1f1d-513f-b197-d71354ab007a"
version = "1.2.12+3"

[[deps.ZygoteRules]]
deps = ["MacroTools"]
git-tree-sha1 = "8c1a8e4dfacb1fd631745552c8db35d0deb09ea0"
uuid = "700de1a5-db45-46bc-99cf-38207098b444"
version = "0.2.2"

[[deps.libblastrampoline_jll]]
deps = ["Artifacts", "Libdl", "OpenBLAS_jll"]
uuid = "8e850b90-86db-534c-a0d3-1478176c7d93"
version = "5.1.1+0"

[[deps.nghttp2_jll]]
deps = ["Artifacts", "Libdl"]
uuid = "8e850ede-7688-5339-a07c-302acd2aaf8d"
version = "1.48.0+0"

[[deps.p7zip_jll]]
deps = ["Artifacts", "Libdl"]
uuid = "3f19e933-33d8-53b3-aaab-bd5110c3b7a0"
version = "17.4.0+0"
"""

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