### A Pluto.jl notebook ###
# v0.19.9

using Markdown
using InteractiveUtils

# ╔═╡ 77b330b5-9fb7-4f74-8306-b147bf052dae
begin
	using CSV, DataFrames
	using MortalityTables, ActuaryUtilities
	using Dates
	using ThreadsX
	using BenchmarkTools
end

# ╔═╡ a7b52cf6-b03d-47e9-818c-22ea8f82f158
using Random

# ╔═╡ 8b86fb4e-2835-4282-8345-6708033423f1
using UnicodePlots

# ╔═╡ c1cacc0f-a789-4fd6-8f1e-a822dd17a66e
md" # Stochastic claims projections demo"

# ╔═╡ 9abe410a-8f6b-4d66-ac0e-570df1e34b47
md" Define a datatype. Not strictly necessary, but will make extending the program with more functions easier."

# ╔═╡ 11f171f8-9137-4e53-9384-f302ad3a773c
md"Type annotations are optional, but providing them is able to coerce the values to be all plain bits (i.e. simple, non-referenced values like arrays are) when the type is constructed. This makes the whole data be stored in the stack and is an example of data-oriented design. It's much slower without the type annotations (~0.5 million policies per second, ~50x slower)"

# ╔═╡ 4310ba76-2a79-49e9-b9c4-78e82871184f
begin
	@enum Sex Female = 1 Male = 2 
	@enum Risk Standard = 1 Preferred = 2
end

# ╔═╡ f4d57736-ad94-4139-a372-dc281be9303a
struct Policy
    id::Int
	sex::Sex
    benefit_base::Float64
    COLA::Float64
    mode::Int
    issue_date::Date
    issue_age::Int
    risk::Risk
end

# ╔═╡ be3ae4ef-661c-40b1-acce-4591cc7e6e5f
md"Load the data:"

# ╔═╡ 14901845-d99b-4a26-947d-63f4c8dd8334
sample_csv_data = 
IOBuffer(
	raw"id,sex,benefit_base,COLA,mode,issue_date,issue_age,risk
		1,M,100000.0,0.03,12,1999-12-05,30,Std
		2,F,200000.0,0.03,12,1999-12-05,30,Pref"
)


# ╔═╡ 5ee1ca24-45aa-47e7-a948-26b4cc819cdb
policies = let 

	# read CSV directly into a dataframe
    # df = CSV.read("sample_inforce.csv",DataFrame) # use local string for notebook
	df = CSV.read(sample_csv_data,DataFrame)

	# map over each row and construct an array of Policy objects
    map(eachrow(df)) do row
        Policy(
            row.id,
			row.sex == "M" ? Male : Female,
            row.benefit_base,
            row.COLA,
            row.mode,
            row.issue_date,
            row.issue_age,
            row.risk == "Std" ? Standard : Preferred,
        )
    end

    
end

# ╔═╡ 89997227-faa5-45fa-aabb-304dee864fd3
md" Define what mortality gets used:"

# ╔═╡ e8d3578b-560b-491e-8ceb-85057f18373e
mort = Dict(
	Male => MortalityTables.table(988).ultimate,
	Female => MortalityTables.table(992).ultimate,
)

# ╔═╡ febebebd-48e6-4645-9c21-699bfb50eb3a
function mortality(pol::Policy,params)
	return params.mortality[pol.sex]
end

# ╔═╡ 23dec098-dda1-4153-bc3f-e1816354888b
md" This defines the core logic of the policy projection and will write the results to the given `out` container (here, a named tuple of arrays).

This is using a threaded approach where it could be operating on any of the computer's available threads, thus acheiving thread-based parallelism (as opposed to multi-processor (multi-machine) or GPU-based computation which requires formulating the problem a bit differently (array/matrix based). For the scale of computation here, I think I'd apply this model of parallelism."

# ╔═╡ ef0b652f-4f73-463a-be25-2e0466286ce1
function pol_project!(out,policy,params)
	# some starting values for the given policy
	dur = duration(policy.issue_date,params.val_date)
	start_age = policy.issue_age + dur - 1
	COLA_factor = (1+policy.COLA)
	cur_benefit = policy.benefit_base * COLA_factor ^ (dur-1)

	# get the right mortality vector
	qs = mortality(policy,params)

	# grab the current thread's id to write to results container without conflicting with other threads
	tid = Threads.threadid()
	
	ω = lastindex(qs) 

	# inbounds turns off bounds-checking, which makes hot loops faster but first write loop without it to ensure you don't create an error (will crash if you have the error without bounds checking)
    @inbounds for t in 1:min(params.proj_length,ω - start_age)
        
		q = qs[start_age + t] # get current mortality
		
		if (rand() < q) 
			return # if dead then just return and don't increment the results anymore
		else
			# pay benefit, add a life to the output count, and increment the benefit for next year
			out.benefits[t,tid] += cur_benefit
			out.lives[t,tid] += 1
			cur_benefit *= COLA_factor
		end
    end
end

# ╔═╡ c3e16d9b-af03-4608-b982-c1c99e638e2d
md" Parameters for our projection:"

# ╔═╡ 8ebc2609-576a-4105-aabe-84f1fe51e8de
params = (
    val_date = Date(2021,12,31),
	proj_length = 100,
	mortality = mort,
)

# ╔═╡ b2f183d5-c739-467f-8e77-2546fff33c0c
md" Check the number of threads we're using:"

# ╔═╡ 84b2c368-c1ba-41ed-ab7c-9677a7293f1a
Threads.nthreads()

# ╔═╡ 055c5cc1-0cf3-407a-af4f-057bc2b8d0e8
function project(policies,params)
	threads = Threads.nthreads()
	benefits = zeros(params.proj_length,threads)
	lives = zeros(Int,params.proj_length,threads)
	out = (;benefits,lives)
	ThreadsX.foreach(policies) do pol
		pol_project!(out,pol,params)
	end
	map(x->vec(reduce(+,x,dims=2)),out)
end

# ╔═╡ 51bdd873-32db-494e-a2f2-1975124f8008
md" Example of single projection:"

# ╔═╡ cfe6a86d-a48e-497c-8a7a-837b16a41931
project(repeat(policies,100_000),params)

# ╔═╡ 8c87a09a-c46f-4143-b2c7-87c51a54332c
md" ## Benchmarking"

# ╔═╡ bde3cee6-8b59-42f8-b450-10044636a563
md"

Using a Macbook Air laptop, about 30 million policies able to be stochastically projected per second:
"

# ╔═╡ e087efbc-9a9f-4e09-ac14-2e4067de458b
let
	policies_to_benchmark = 3_000_000
	# adjust the `repeat` depending on how many policies are already in the array
	# to match the target number for the benchmark
	n = policies_to_benchmark ÷ length(policies)
	
	@benchmark project(p,r) setup=(p=repeat($policies,$n);r=$params)
end

# ╔═╡ 9e45b261-4e29-48d7-99fe-1fc623b5a757
md""" ## Stochastic Set

Loop through and calculate the reults `n` times (this is only running the two policies in the sample data" `n` times).
"""

# ╔═╡ 778f030b-aab1-4191-8902-3f10f402b82e
function stochastic_proj(policies,params,n)

	ThreadsX.map(1:n) do i
		project(policies,params)
	end
end

# ╔═╡ 73ead2b9-733d-470c-a863-9aa4397c35cd
stoch = stochastic_proj(policies,params,1000)

# ╔═╡ 0e8d3fe0-5df3-4db9-88a6-fe4f738211c0
let
	v = [pv(0.03,s.benefits) for s in stoch]
	histogram(v,nbins=15)
end

# ╔═╡ 786312c8-2a40-44db-96cd-a1edc2f29e9a
md" # Further Optimization

In no particular order:
- the RNG could be made faster: https://bkamins.github.io/julialang/2020/11/20/rand.html
- Could make the stochastic set distributed, but at the current speed the overhead of distributed computing is probably more time than it would save. Same thing with GPU projections
- ...

"

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version = "0.9.6"

[[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"

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

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

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

[[UnicodePlots]]
deps = ["ColorTypes", "Contour", "Crayons", "Dates", "FileIO", "FreeTypeAbstraction", "LinearAlgebra", "MarchingCubes", "NaNMath", "Printf", "SparseArrays", "StaticArrays", "StatsBase", "Unitful"]
git-tree-sha1 = "4b18663db312f47f9729935066a377e5c6a9bdfb"
uuid = "b8865327-cd53-5732-bb35-84acbb429228"
version = "2.11.2"

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

[[WeakRefStrings]]
deps = ["DataAPI", "InlineStrings", "Parsers"]
git-tree-sha1 = "b1be2855ed9ed8eac54e5caff2afcdb442d52c23"
uuid = "ea10d353-3f73-51f8-a26c-33c1cb351aa5"
version = "1.4.2"

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

[[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"

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

[[Yields]]
deps = ["BSplineKit", "ForwardDiff", "LinearAlgebra", "Roots", "UnicodePlots"]
git-tree-sha1 = "a015831cafd75a79445ca9cbdf025112b8c41f56"
uuid = "d7e99b2f-e7f3-4d9e-9f01-2338fc023ad3"
version = "1.1.2"

[[Zlib_jll]]
deps = ["Libdl"]
uuid = "83775a58-1f1d-513f-b197-d71354ab007a"

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

[[nghttp2_jll]]
deps = ["Artifacts", "Libdl"]
uuid = "8e850ede-7688-5339-a07c-302acd2aaf8d"

[[p7zip_jll]]
deps = ["Artifacts", "Libdl"]
uuid = "3f19e933-33d8-53b3-aaab-bd5110c3b7a0"
"""

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