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