Added Project and Report
This commit is contained in:
elvis
291
project/testing/L-BFGS/LBFGS_3D.ipynb
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291
project/testing/L-BFGS/LBFGS_3D.ipynb
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{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": 2,
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"id": "7b26750c-46f3-4650-aad9-759312cb7486",
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"metadata": {},
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"outputs": [],
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"source": [
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"include(\"../../L-BFGS/OracleFunction.jl\")\n",
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"include(\"../../L-BFGS/LBFGS.jl\")\n",
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"include(\"../../utilities/genFunc.jl\")\n",
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"using .LBFGS\n",
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"using .OracleFunction\n",
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"using LinearAlgebra, BenchmarkTools, CSV, DataFrames\n",
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"\n",
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"baseDir = joinpath(\"../\", \"results/LBFGS/3D/\")\n",
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"mkpath(baseDir);"
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]
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},
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{
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"cell_type": "markdown",
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"id": "468b2db2",
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"metadata": {},
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"source": [
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"## 3D PLOTS"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "f96101fb-0029-48c9-b5f0-4b9b9f45feb6",
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"metadata": {
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"scrolled": true
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},
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"outputs": [],
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"source": [
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"### time with respect to lambda and epsilon\n",
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"\n",
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"# parameters\n",
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"lambdas = 10. .^(-15:6)\n",
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"epss = 10. .^(-15:-6)\n",
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"memsize = 7\n",
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"m = 300\n",
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"n = 20\n",
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"\n",
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"\n",
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"# output csv\n",
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"outputvsc = joinpath(baseDir, \"statisticsLBFGS-lambda-eps-m\" * string(m) * \"n\" * string(n) * \"--time.csv\");\n",
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"accData = Dict(\n",
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" :lambda => Array{Float64}(undef, 0),\n",
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" :epsilon => Array{Float64}(undef, 0),\n",
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" :memsize => Array{Int64}(undef, 0),\n",
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" :meantime => Array{Float64}(undef, 0),\n",
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" :stdtime => Array{Float64}(undef, 0)\n",
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" )\n",
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"\n",
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"# computation\n",
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"for (λ, ϵ) ∈ Iterators.product(lambdas, epss) |> collect\n",
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" ls = genFunc(:exactRandDataset, λ=λ, m=m, n=n) |> LeastSquaresF\n",
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"\n",
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" t = @benchmark begin\n",
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" _ = LimitedMemoryBFGS($ls, ϵ=$ϵ, m=memsize)\n",
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" end samples=10 evals=1\n",
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"\n",
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" push!(accData[:lambda], λ)\n",
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" push!(accData[:epsilon], ϵ)\n",
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" push!(accData[:memsize], memsize)\n",
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" push!(accData[:meantime], mean(t.times))\n",
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" push!(accData[:stdtime], std(t.times))\n",
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" println(\"Done: λ \" * string(λ) * \" - ϵ \" * string(ϵ))\n",
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" flush(stdout)\n",
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"end\n",
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"\n",
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"CSV.write(outputvsc, DataFrame(accData));"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "5b36db4f-2659-4591-8640-cd5071788f67",
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"metadata": {
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"scrolled": true
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},
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"outputs": [],
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"source": [
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"### iterations with respect to lambda and epsilon\n",
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"\n",
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"# parameters\n",
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"lambdas = 10. .^(-15:6)\n",
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"epss = 10. .^(-15:-6)\n",
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"memsize = 7\n",
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"maxIter = 10000\n",
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"m = 300\n",
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"n = 20\n",
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"\n",
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"\n",
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"# output csv\n",
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"outputvsc = joinpath(baseDir, \"statisticsLBFGS-lambda-eps-m\" * string(m) * \"n\" * string(n) * \"--iterations.csv\");\n",
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"accData = Dict(\n",
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" :lambda => Array{Float64}(undef, 0),\n",
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" :epsilon => Array{Float64}(undef, 0),\n",
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" :memsize => Array{Int64}(undef, 0),\n",
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" :iterations => Array{Int64}(undef, 0)\n",
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" )\n",
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"\n",
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"# computation\n",
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"for (λ, ϵ) ∈ Iterators.product(lambdas, epss) |> collect\n",
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" ls = genFunc(:exactRandDataset, λ=λ, m=m, n=n) |> LeastSquaresF\n",
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"\n",
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" t = maxIter - LimitedMemoryBFGS(ls, ϵ=ϵ, MaxEvaluations=maxIter, m=memsize)[:RemainingEvaluations]\n",
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"\n",
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" push!(accData[:lambda], λ)\n",
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" push!(accData[:epsilon], ϵ)\n",
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" push!(accData[:memsize], memsize)\n",
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" push!(accData[:iterations], t)\n",
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" println(\"Done: λ \" * string(λ) * \" - ϵ \" * string(ϵ))\n",
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" flush(stdout)\n",
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"end\n",
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"\n",
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"CSV.write(outputvsc, DataFrame(accData));"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "f3bb9382-8e68-49aa-81ac-6e3e3fcdf85b",
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"metadata": {
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"scrolled": true
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},
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"outputs": [],
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"source": [
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"### error with respect to lambda and epsilon\n",
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"\n",
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"# parameters\n",
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"lambdas = 10. .^(-15:6)\n",
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"epss = 10. .^(-15:-6)\n",
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"memsize = 7\n",
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"maxIter = 10000\n",
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"m = 300\n",
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"n = 20\n",
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"\n",
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"\n",
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"# output csv\n",
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"outputvsc = joinpath(baseDir, \"statisticsLBFGS-lambda-eps-m\" * string(m) * \"n\" * string(n) * \"--error.csv\");\n",
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"accData = Dict(\n",
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" :lambda => Array{Float64}(undef, 0),\n",
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" :epsilon => Array{Float64}(undef, 0),\n",
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" :memsize => Array{Int64}(undef, 0),\n",
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" :relative => Array{Float64}(undef, 0),\n",
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" :residual => Array{Float64}(undef, 0)\n",
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" )\n",
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"\n",
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"# computation\n",
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"for (λ, ϵ) ∈ Iterators.product(lambdas, epss) |> collect\n",
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" gf = genFunc(:exactRandDataset, λ=λ, m=m, n=n)\n",
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" ls = LeastSquaresF(gf)\n",
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"\n",
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" t = LimitedMemoryBFGS(ls, ϵ=ϵ, MaxEvaluations=maxIter, m=memsize)\n",
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"\n",
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" relative_error = norm(t[:x] - gf[:w_star]) / norm(gf[:w_star])\n",
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" residual = norm(gf[:X_hat] * t[:x] - gf[:y_hat]) / norm(gf[:y_hat])\n",
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"\n",
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" push!(accData[:lambda], λ)\n",
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" push!(accData[:epsilon], ϵ)\n",
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" push!(accData[:memsize], memsize)\n",
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" push!(accData[:relative], relative_error)\n",
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" push!(accData[:residual], residual)\n",
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" println(\"Done: λ \" * string(λ) * \" - ϵ \" * string(ϵ))\n",
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" flush(stdout)\n",
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"end\n",
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"\n",
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"CSV.write(outputvsc, DataFrame(accData));"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "cff97997-96a4-4223-bed9-00663a9a2a6c",
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"metadata": {
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"scrolled": true
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},
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"outputs": [],
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"source": [
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"### iterations with respect to epsilon and memory size\n",
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"\n",
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"# parameters\n",
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"λ = 10^-3\n",
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"epss = 10. .^(-15:-6)\n",
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"memsizes = [1, 3, 5, 7, 9, 11, 15, 20, 40, 60]\n",
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"maxIter = 10000\n",
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"m = 300\n",
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"n = 20\n",
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"\n",
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"\n",
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"# output csv\n",
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"outputvsc = joinpath(baseDir, \"statisticsLBFGS-eps-mem-m\" * string(m) * \"n\" * string(n) * \"--iterations.csv\");\n",
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"accData = Dict(\n",
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" :lambda => Array{Float64}(undef, 0),\n",
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" :epsilon => Array{Float64}(undef, 0),\n",
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" :memsize => Array{Int64}(undef, 0),\n",
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" :iterations => Array{Int64}(undef, 0)\n",
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" )\n",
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"\n",
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"# computation\n",
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"for (ϵ, memsize) ∈ Iterators.product(epss, memsizes) |> collect\n",
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" gf = genFunc(:exactRandDataset, λ=λ, m=m, n=n)\n",
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" ls = LeastSquaresF(gf)\n",
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"\n",
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" t = maxIter - LimitedMemoryBFGS(ls, ϵ=ϵ, MaxEvaluations=maxIter, m=memsize)[:RemainingEvaluations]\n",
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"\n",
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" push!(accData[:lambda], λ)\n",
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" push!(accData[:epsilon], ϵ)\n",
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" push!(accData[:memsize], memsize)\n",
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" push!(accData[:iterations], t)\n",
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" println(\"Done: mem \" * string(memsize) * \" - ϵ \" * string(ϵ))\n",
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" flush(stdout)\n",
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"end\n",
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"\n",
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"CSV.write(outputvsc, DataFrame(accData));"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "65a5b24b-ad25-4f13-8ebc-433e5e91eb75",
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"metadata": {
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"scrolled": true
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},
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"outputs": [],
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"source": [
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"### error with respect to epsilon and memory size\n",
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"\n",
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"# parameters\n",
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"λ = 10^-3\n",
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"epss = 10. .^(-15:-6)\n",
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"memsizes = [1, 3, 5, 7, 9, 11, 15, 20, 40, 60]\n",
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"maxIter = 10000\n",
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"m = 300\n",
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"n = 20\n",
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"\n",
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"\n",
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"# output csv\n",
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"outputvsc = joinpath(baseDir, \"statisticsLBFGS-eps-mem-m\" * string(m) * \"n\" * string(n) * \"--error.csv\");\n",
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"accData = Dict(\n",
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" :lambda => Array{Float64}(undef, 0),\n",
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" :epsilon => Array{Float64}(undef, 0),\n",
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" :memsize => Array{Int64}(undef, 0),\n",
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" :relative => Array{Float64}(undef, 0),\n",
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" :residual => Array{Float64}(undef, 0)\n",
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" )\n",
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"\n",
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"# computation\n",
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"for (ϵ, memsize) ∈ Iterators.product(epss, memsizes) |> collect\n",
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" gf = genFunc(:exactRandDataset, λ=λ, m=m, n=n)\n",
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" ls = LeastSquaresF(gf)\n",
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"\n",
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" t = LimitedMemoryBFGS(ls, ϵ=ϵ, MaxEvaluations=maxIter, m=memsize)\n",
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"\n",
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" relative_error = norm(t[:x] - gf[:w_star]) / norm(gf[:w_star])\n",
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" residual = norm(gf[:X_hat] * t[:x] - gf[:y_hat]) / norm(gf[:y_hat])\n",
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"\n",
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" push!(accData[:lambda], λ)\n",
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" push!(accData[:epsilon], ϵ)\n",
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" push!(accData[:memsize], memsize)\n",
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" push!(accData[:relative], relative_error)\n",
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" push!(accData[:residual], residual)\n",
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" println(\"Done: mem \" * string(memsize) * \" - ϵ \" * string(ϵ))\n",
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" flush(stdout)\n",
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"end\n",
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"\n",
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"CSV.write(outputvsc, DataFrame(accData));"
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]
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Julia 1.9.3",
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"language": "julia",
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"name": "julia-1.9"
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},
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"language_info": {
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"file_extension": ".jl",
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"mimetype": "application/julia",
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"name": "julia",
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"version": "1.9.3"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 5
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}
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