Logistic regression

Binary classification via logistic regression in Julia.

This page comes from a single Julia file: logistic1.jl.

You can access the source code for such Julia documentation using the 'Edit on GitHub' link in the top right. You can view the corresponding notebook in nbviewer here: logistic1.ipynb, or open it in binder here: logistic1.ipynb.

Setup

Add the Julia packages used in this demo. Change false to true in the following code block if you are using any of the following packages for the first time.

if false
    import Pkg
    Pkg.add([
        "InteractiveUtils"
        "LaTeXStrings"
        "LinearAlgebra"
        "MIRTjim"
        "Plots"
        "Random"
        "StatsBase"
    ])
end

Tell Julia to use the following packages. Run Pkg.add() in the preceding code block first, if needed.

using InteractiveUtils: versioninfo
using LaTeXStrings
using LinearAlgebra: dot, eigvals
using MIRTjim: prompt
using Plots: default, gui, savefig
using Plots: plot, plot!, scatter, scatter!
using Random: seed!
using StatsBase: mean
default(); default(markersize=6, linewidth=2, markerstrokecolor=:auto, label="",
 tickfontsize=12, labelfontsize=18, legendfontsize=18, titlefontsize=18)

The following line is helpful when running this file as a script; this way it will prompt user to hit a key after each figure is displayed.

isinteractive() ? prompt(:prompt) : prompt(:draw)

:draw

Data

Generate synthetic data from two classes

if !@isdefined(yy)
    seed!(0)
    n0 = 60
    n1 = 50
    mu0 = [-1, 1]
    mu1 = [1, -1]
    v0 = mu0 .+ randn(2,n0) # class -1
    v1 = mu1 .+ randn(2,n1) # class 1
    nex = 0
    if true # 2017-01-18
        nex = 4 # extra dim (beyond the 2 shown) to make "larger scale"
        v0 = [v0; rand(nex,n0)] # (2+nex, n0)
        v1 = [v1; rand(nex,n1)] # (2+nex, n1)
    end
    M = n0 + n1 # how many samples
    yy = [-ones(n0); ones(n1)] # (M) labels
    vv = [[v0 v1]; ones(1,n0+n1)] # (npar, M) training data
    npar = 3 + nex # unknown parameters
end;

scatter plot and initial decision boundary

if !@isdefined(ps)
    x0 = [-1; 3; rand(nex); 5]
    v1p = range(-1,1,101) * 4
    v2p_fun = x -> @. (-x[end] - x[1] * v1p) / x[2]

    ps = plot(aspect_ratio = 1, size = (550, 500), legend=:topright,
     xaxis = (L"v_1", (-4, 4), [-4 -1 0 1 4]),
     yaxis = (L"v_2", (-4, 4), [-4 -1 0 1 4]),
    )
    plot!(v1p, v2p_fun(x0), color=:red, label="initial")
    plot!(v1p, v1p, color=:yellow, label="ideal")
    scatter!(v0[1,:], v0[2,:], color=:green, alpha=0.7)
    scatter!(v1[1,:], v1[2,:], color=:blue, marker=:square, alpha=0.7)
    # savefig(ps, "demo_fgm1_ogm1_s0.pdf")
end
plot(ps)

prompt()

Cost function

Logistic regression with Tikhonov regularization:

\[f(x) = 1_M' h.(A x) + β/2 ‖ x ‖_2^2\]

where $h(z) = log(1 + e^{-z})$ is the logistic loss function.

Its gradient is $∇ f(x) = A' \dot{h}.(A x) + β x$, and its Lipschitz constant is $‖A‖_2^2 / 4 + β$.

if !@isdefined(kost)
    pot = (t) -> log(1 + exp(-t)) # logistic
    dpot = (t) -> -1 / (exp(t) + 1)
    tmp = vv * vv' # (npar, npar) covariance
    tmp = eigvals(tmp)
    @show maximum(tmp) / minimum(tmp)
    pLip = maximum(tmp) / 4 # 1/4 comes from logistic curvature

    reg = 2^0
    Lip = pLip + reg # Lipschitz constant

    A = yy .* vv'
    gfun = x -> A' * dpot.(A * x) + reg * x # gradient
    if false
        tmp = gfun(x0)
        @show size(tmp)
    end

    kost = x -> sum(pot, A * x, dims=1) .+ reg/2 * sum(abs2, x, dims=1)
end;

maximum(tmp) / minimum(tmp) = 79.5564516910791

GD

Iterate GD

tol = 1e-6
tol_gd = tol
tol_n1 = tol
tol_o1 = tol

function gd(x0, gfun::Function, niter::Int)
    xg = copy(x0)
    xgs = copy(xg)
    for n in 1:niter
        xold = xg
        xg -= 1/Lip * gfun(xg)
        if false && (norm(xg - xold, Inf) / norm(x0, Inf) < tol_gd)
            @show n
            break
        end
        any(isnan, xg) && throw("nan")
        # projected GD ??
        # xg = xg / norm(xg) # decision boundary unaffected by norm!
        xgs = [xgs xg] # archive
    end
    return xgs
end

if !@isdefined(xgs)
    niter_gd = 300
    xgs = gd(x0, gfun, niter_gd)
    pgs = plot(xgs', xlabel="Iteration", title = "GD")
end
plot(pgs)

prompt()

Nesterov FGM

do_restart = true;

function fgm(x0, grad, Lip::Real, niter::Int)
    re_nest = Int[]
    told = 1
    x = copy(x0)
    zold = copy(x0)
    xns = copy(x)
    for n in 1:niter
        xold = copy(x)
        grad = gfun(x)
        znew = x - 1/Lip * grad
        zdiff = znew - zold # dk - for restart

        tnew = 1/2 * (1 + sqrt(1 + 4 * told^2))

        x = znew + (told - 1) / tnew * (znew - zold)
        zold = copy(znew)
        told = tnew
        if false && (norm(x - xold, Inf) / norm(x0, Inf) < tol_n1)
            @show n
            break
        end
        any(isnan, x) && throw("nan")

        if do_restart && (dot(grad, zdiff) > 0) # dk new version
            told = 1
            x = copy(znew) # check
            zold = copy(x)
            @show "nest. restart", n
            push!(re_nest, n)
        end
        xns = [xns x] # archive
    end
    return xns, re_nest
end

if !@isdefined(xns)
    niter_n1 = 300
    niter_n1 = 500 # nex
    xns, re_nest = fgm(x0, gfun, Lip, niter_n1)
    pns = plot(xns', xlabel = "Iteration", title = "FGM")
end
plot(pns)

prompt()

OGM

Optimized gradient method

function ogm(
    x0,
    gfun,
    Lip::Real,
    niter::Int,
)
    re_ogm1 = Int[]
    told = 1
    x = copy(x0)
    zold = copy(x0)
    x1s = copy(x)
    for n in 1:niter
        xold = x
        grad = gfun(x)
        znew = x - 1/Lip * grad

        tnew = 1/2 * (1 + sqrt(1 + 4 * told^2))

        x = znew + (told - 1) / tnew * (znew - zold) + told/tnew * (znew - xold)
        zdiff = znew - zold # dk - for restart
        zold = znew
        told = tnew
        if false && (norm(x - xold, Inf) / norm(x0, Inf) < tol_n1)
            @show n
            break
        end
        any(isnan, x) && throw("nan")

        if do_restart && (dot(grad, zdiff) > 0) # dk new version
            push!(re_ogm1, n)
            told = 1
            x = znew # check
            zold = x # dk fixed from x0
            @info "ogm1 restart $n"
        end
        x1s = [x1s x] # archive
    end
    return x1s, re_ogm1
end


if !@isdefined(x1s)
    niter_o1 = 300
    niter_o1 = 500 # nex
    x1s, re_ogm1 = ogm(x0, gfun, Lip, niter_o1)
    po1 = plot(x1s', xlabel = "Iteration", title = "OGM")
end
plot(po1)

prompt()

impartial version of x ͚

xh_tmp = [xgs[:,end] xns[:,end] x1s[:,end]]
xh = vec(mean(xh_tmp[:,2:3], dims=2)); # GD too slow to include

plot cost

extra = do_restart ? " (restart)" : ""
pc = plot(xaxis=("iteration", (0,10)), yaxis=("Cost function",))
plot!(0:niter_gd, vec(kost(xgs)) .- kost(xh), label = "GD" * extra)
plot!(0:niter_n1, vec(kost(xns)) .- kost(xh), label = "FGM" * extra)
plot!(0:niter_o1, vec(kost(x1s)) .- kost(xh), label = "OGM1" * extra)

prompt()

Plot decision boundaries

if true
    psh = deepcopy(ps)
    v2p = @. (-xh[end] - xh[1] * v1p) / xh[2]
    plot!(psh, v1p, v2p, color = :magenta, label="final")
# savefig(psh, "demo-fgm1-fgm1a.pdf")
end
plot(psh)

prompt()

Plot iterate convergence

efun1 = (x) -> vec(sqrt.(sum(abs2, x .- xh, dims=1)))
efun = (x) -> do_restart ? log10.(efun1(x)) : efun1(x)
ifun = (x) -> 0:(size(x,2)-1);

pic = plot(
 xaxis = ("Iteration", (0, 40+10*nex), 0:20:80),
 yaxis = do_restart ?
  (L"\log_{10}(‖ \mathbf{x}_k - \mathbf{x}_* ‖)", (-3, 1), -3:1) :
  (L"‖ \mathbf{x}_k - \mathbf{x}_* ‖", (0, 8), [-1, 0, 8]),
 legend = :topright,
)
plot!(ifun(xgs), efun(xgs), color=:green, label = "GD")
plot!(ifun(xns), efun(xns), color=:blue, label = "Nesterov FGM" * extra)
plot!(ifun(x1s), efun(x1s), color=:red, label = "OGM1" * extra)
if do_restart
    scatter!(re_nest, efun(xns[:, re_nest .+ 1]), color=:blue)
    scatter!(re_ogm1, efun(x1s[:, re_ogm1 .+ 1]), color=:red)
end
plot(pic)

prompt()

# savefig demo_fgm1_ogm1c # restart
# savefig demo_fgm1_ogm1b # no restart

# todo: compare with LBFGS

Reproducibility

This page was generated with the following version of Julia:

using InteractiveUtils: versioninfo
io = IOBuffer(); versioninfo(io); split(String(take!(io)), '\n')

11-element Vector{SubString{String}}:
 "Julia Version 1.11.2"
 "Commit 5e9a32e7af2 (2024-12-01 20:02 UTC)"
 "Build Info:"
 "  Official https://julialang.org/ release"
 "Platform Info:"
 "  OS: Linux (x86_64-linux-gnu)"
 "  CPU: 4 × AMD EPYC 7763 64-Core Processor"
 "  WORD_SIZE: 64"
 "  LLVM: libLLVM-16.0.6 (ORCJIT, znver3)"
 "Threads: 1 default, 0 interactive, 1 GC (on 4 virtual cores)"
 ""

And with the following package versions

import Pkg; Pkg.status()

Status `~/work/book-la-demo/book-la-demo/docs/Project.toml`
  [6e4b80f9] BenchmarkTools v1.5.0
  [aaaa29a8] Clustering v0.15.7
  [35d6a980] ColorSchemes v3.27.1
⌅ [3da002f7] ColorTypes v0.11.5
⌃ [c3611d14] ColorVectorSpace v0.10.0
⌅ [717857b8] DSP v0.7.10
  [72c85766] Demos v0.1.0 `~/work/book-la-demo/book-la-demo`
  [e30172f5] Documenter v1.8.0
  [4f61f5a4] FFTViews v0.3.2
  [7a1cc6ca] FFTW v1.8.0
  [587475ba] Flux v0.15.2
  [a09fc81d] ImageCore v0.10.5
  [71a99df6] ImagePhantoms v0.8.1
  [b964fa9f] LaTeXStrings v1.4.0
  [7031d0ef] LazyGrids v1.0.0
  [599c1a8e] LinearMapsAA v0.12.0
  [98b081ad] Literate v2.20.1
  [7035ae7a] MIRT v0.18.2
  [170b2178] MIRTjim v0.25.0
  [eb30cadb] MLDatasets v0.7.18
  [efe261a4] NFFT v0.13.5
  [6ef6ca0d] NMF v1.0.3
  [15e1cf62] NPZ v0.4.3
  [0b1bfda6] OneHotArrays v0.2.6
  [429524aa] Optim v1.10.0
  [91a5bcdd] Plots v1.40.9
  [f27b6e38] Polynomials v4.0.12
  [2913bbd2] StatsBase v0.34.4
  [d6d074c3] VideoIO v1.1.1
  [b77e0a4c] InteractiveUtils v1.11.0
  [37e2e46d] LinearAlgebra v1.11.0
  [44cfe95a] Pkg v1.11.0
  [9a3f8284] Random v1.11.0
Info Packages marked with ⌃ and ⌅ have new versions available. Those with ⌃ may be upgradable, but those with ⌅ are restricted by compatibility constraints from upgrading. To see why use `status --outdated`

This page was generated using Literate.jl.