using Jchemo, JchemoData
using JLD2, CairoMakie

Data importation

path_jdat = dirname(dirname(pathof(JchemoData)))
db = joinpath(path_jdat, "data/tecator.jld2") 
@load db dat
@names dat

(:X, :Y)

X = dat.X
@head X

... (178, 100)

Y = dat.Y 
@head Y

... (178, 4)

typ = Y.typ
tab(typ)

OrderedCollections.OrderedDict{String, Int64} with 3 entries:
  "test"  => 31
  "train" => 115
  "val"   => 32

wlst = names(X)
wl = parse.(Float64, wlst) 
#plotsp(X, wl; xlabel = "Wavelength (nm)", ylabel = "Absorbance").f

100-element Vector{Float64}:
  850.0
  852.0
  854.0
  856.0
  858.0
  860.0
  862.0
  864.0
  866.0
  868.0
    ⋮
 1032.0
 1034.0
 1036.0
 1038.0
 1040.0
 1042.0
 1044.0
 1046.0
 1048.0

Preprocessing

model1 = snv()
model2 = savgol(npoint = 15, deriv = 2, degree = 3)
model = pip(model1, model2)
fit!(model, X)
@head Xp = transf(model, X) 
#plotsp(Xp, wl; xlabel = "Wavelength (nm)", ylabel = "Absorbance").f

3×100 Matrix{Float64}:
 0.000397076  0.000495203  0.000598623  …  0.00827138  0.00917311  0.00946072
 0.00242055   0.00244366   0.00234233      0.00580631  0.00689249  0.00749316
 0.0011927    0.00122721   0.00120098      0.0101019   0.0108142   0.0108444
... (178, 100)

Split Tot to Train/Test

The model is fitted on Train, and the generalization error is estimated on Test. In this example, Train is already defined in variable typ of the dataset, and Test is defined by the remaining samples. But Tot could also be split a posteriori, for instance by sampling (random, systematic or any other designs). See for instance functions samprand, sampsys, etc.

s = typ .== "train"
Xtrain = Xp[s, :] 
Ytrain = Y[s, :]
Xtest = rmrow(Xp, s)
Ytest = rmrow(Y, s)
ntrain = nro(Xtrain)
ntest = nro(Xtest)
ntot = ntrain + ntest
(ntot = ntot, ntrain, ntest)

(ntot = 178, ntrain = 115, ntest = 63)

Working response y

namy = names(Y)[1:3]
j = 2  
nam = namy[j]    # work on the j-th y-variable
ytrain = Ytrain[:, nam]
ytest = Ytest[:, nam]

63-element Vector{Float64}:
 29.8
  1.4
  4.6
 11.0
 17.0
 22.4
 27.9
 46.5
  6.1
  2.0
  ⋮
 18.1
 19.4
 24.8
 27.2
 28.4
 31.3
 33.8
 35.5
 42.5

CV-Segments for model tuning

Two methods can be used to build the CV segments within Train (for the same total number of segments, these two methods return close resuts):

• (1) Replicated K-fold CV

  • Train is splitted in a number of K folds (segments),

  • and this split can be replicated (==> replicated K-Fold CV).

K = 3     # nb. folds (segments)
rep = 25  # nb. replications
segm = segmkf(ntrain, K; rep = rep)

25-element Vector{Vector{Vector{Int64}}}:
 [[3, 4, 5, 12, 14, 16, 19, 25, 28, 31  …  95, 96, 97, 103, 104, 105, 106, 109, 112, 114], [6, 7, 8, 11, 15, 18, 20, 23, 26, 29  …  84, 86, 94, 98, 102, 107, 108, 110, 113, 115], [1, 2, 9, 10, 13, 17, 21, 22, 24, 27  …  82, 85, 87, 88, 89, 92, 99, 100, 101, 111]]
 [[4, 6, 17, 18, 19, 21, 23, 26, 27, 29  …  92, 93, 96, 104, 107, 110, 112, 113, 114, 115], [12, 13, 14, 16, 20, 24, 25, 28, 34, 39  …  91, 95, 97, 99, 100, 101, 102, 105, 106, 108], [1, 2, 3, 5, 7, 8, 9, 10, 11, 15  …  82, 83, 85, 88, 89, 94, 98, 103, 109, 111]]
 [[2, 8, 13, 15, 22, 24, 26, 28, 29, 30  …  90, 93, 96, 100, 103, 105, 106, 107, 108, 115], [3, 4, 5, 6, 9, 11, 12, 14, 16, 17  …  69, 81, 82, 86, 91, 95, 102, 110, 111, 112], [1, 7, 10, 19, 20, 23, 27, 33, 35, 45  …  92, 94, 97, 98, 99, 101, 104, 109, 113, 114]]
 [[1, 3, 9, 11, 12, 13, 16, 17, 19, 22  …  84, 85, 91, 92, 94, 98, 100, 101, 112, 114], [5, 7, 18, 21, 24, 30, 31, 34, 38, 40  …  89, 90, 93, 96, 97, 99, 102, 104, 107, 115], [2, 4, 6, 8, 10, 14, 15, 20, 23, 26  …  87, 95, 103, 105, 106, 108, 109, 110, 111, 113]]
 [[2, 4, 6, 12, 14, 16, 17, 23, 29, 30  …  85, 87, 93, 94, 96, 97, 99, 105, 110, 115], [1, 3, 7, 8, 18, 24, 25, 26, 32, 37  …  90, 91, 95, 98, 100, 104, 107, 109, 111, 113], [5, 9, 10, 11, 13, 15, 19, 20, 21, 22  …  86, 89, 92, 101, 102, 103, 106, 108, 112, 114]]
 [[4, 6, 7, 8, 9, 11, 12, 14, 18, 21  …  79, 94, 96, 98, 101, 102, 103, 111, 114, 115], [2, 3, 10, 13, 17, 19, 24, 25, 26, 27  …  66, 67, 78, 80, 87, 100, 104, 107, 108, 113], [1, 5, 15, 16, 20, 34, 36, 37, 39, 45  …  92, 93, 95, 97, 99, 105, 106, 109, 110, 112]]
 [[3, 4, 8, 17, 18, 19, 21, 25, 26, 28  …  99, 100, 101, 103, 105, 108, 110, 112, 114, 115], [1, 2, 10, 13, 14, 16, 20, 22, 24, 34  …  86, 88, 89, 90, 91, 92, 102, 106, 111, 113], [5, 6, 7, 9, 11, 12, 15, 23, 27, 31  …  71, 73, 74, 75, 79, 80, 87, 104, 107, 109]]
 [[5, 12, 16, 18, 24, 27, 28, 29, 31, 35  …  92, 94, 96, 101, 102, 104, 109, 111, 112, 113], [3, 4, 6, 7, 10, 11, 13, 15, 21, 26  …  85, 86, 93, 98, 100, 103, 107, 108, 110, 114], [1, 2, 8, 9, 14, 17, 19, 20, 22, 23  …  87, 89, 90, 91, 95, 97, 99, 105, 106, 115]]
 [[1, 7, 9, 12, 13, 15, 16, 21, 28, 29  …  86, 87, 92, 93, 94, 97, 100, 102, 111, 113], [2, 8, 10, 11, 14, 19, 23, 24, 25, 27  …  95, 98, 101, 104, 106, 107, 108, 110, 114, 115], [3, 4, 5, 6, 17, 18, 20, 22, 26, 30  …  78, 79, 85, 89, 96, 99, 103, 105, 109, 112]]
 [[1, 3, 4, 6, 8, 10, 16, 18, 21, 22  …  84, 88, 90, 93, 99, 102, 104, 106, 111, 115], [5, 11, 17, 19, 20, 25, 27, 30, 39, 40  …  94, 95, 96, 98, 103, 107, 108, 110, 113, 114], [2, 7, 9, 12, 13, 14, 15, 26, 28, 31  …  82, 86, 87, 91, 97, 100, 101, 105, 109, 112]]
 ⋮
 [[5, 6, 8, 12, 13, 21, 22, 27, 28, 30  …  89, 91, 97, 98, 104, 108, 109, 110, 113, 114], [1, 4, 11, 16, 17, 19, 23, 25, 26, 32  …  84, 90, 93, 94, 100, 105, 106, 107, 111, 115], [2, 3, 7, 9, 10, 14, 15, 18, 20, 24  …  86, 87, 92, 95, 96, 99, 101, 102, 103, 112]]
 [[5, 10, 11, 12, 13, 15, 26, 28, 30, 31  …  79, 80, 84, 90, 91, 95, 104, 111, 112, 115], [4, 6, 14, 17, 18, 20, 22, 27, 32, 36  …  89, 93, 94, 97, 98, 99, 100, 101, 106, 107], [1, 2, 3, 7, 8, 9, 16, 19, 21, 23  …  92, 96, 102, 103, 105, 108, 109, 110, 113, 114]]
 [[10, 17, 23, 25, 26, 29, 31, 33, 34, 37  …  92, 97, 100, 101, 103, 105, 107, 109, 114, 115], [3, 4, 5, 6, 8, 9, 12, 24, 27, 30  …  80, 85, 89, 96, 98, 99, 108, 111, 112, 113], [1, 2, 7, 11, 13, 14, 15, 16, 18, 19  …  84, 86, 91, 93, 94, 95, 102, 104, 106, 110]]
 [[2, 4, 12, 15, 19, 26, 27, 29, 30, 37  …  85, 93, 94, 95, 99, 100, 104, 110, 114, 115], [5, 6, 7, 8, 9, 11, 20, 21, 22, 25  …  77, 89, 92, 96, 102, 103, 105, 108, 109, 112], [1, 3, 10, 13, 14, 16, 17, 18, 23, 24  …  88, 90, 91, 97, 98, 101, 106, 107, 111, 113]]
 [[3, 7, 11, 12, 15, 21, 22, 23, 25, 27  …  97, 100, 101, 103, 104, 106, 107, 109, 113, 114], [1, 2, 6, 8, 10, 13, 18, 19, 24, 28  …  85, 88, 89, 94, 96, 98, 105, 111, 112, 115], [4, 5, 9, 14, 16, 17, 20, 26, 30, 32  …  78, 81, 86, 91, 93, 95, 99, 102, 108, 110]]
 [[5, 7, 11, 12, 15, 16, 17, 24, 28, 30  …  84, 85, 91, 93, 96, 101, 104, 109, 113, 115], [3, 8, 9, 18, 19, 22, 23, 26, 27, 29  …  82, 86, 88, 95, 98, 100, 103, 105, 108, 114], [1, 2, 4, 6, 10, 13, 14, 20, 21, 25  …  92, 94, 97, 99, 102, 106, 107, 110, 111, 112]]
 [[1, 3, 4, 7, 8, 12, 14, 19, 20, 21  …  75, 81, 82, 89, 95, 98, 99, 104, 110, 114], [10, 11, 16, 18, 24, 31, 33, 34, 35, 40  …  96, 100, 103, 105, 107, 108, 109, 111, 112, 115], [2, 5, 6, 9, 13, 15, 17, 22, 25, 27  …  86, 90, 92, 93, 94, 97, 101, 102, 106, 113]]
 [[4, 8, 14, 26, 27, 28, 32, 35, 37, 41  …  95, 96, 97, 99, 100, 102, 109, 111, 113, 114], [3, 5, 6, 7, 9, 11, 18, 20, 21, 22  …  78, 79, 82, 90, 91, 94, 106, 110, 112, 115], [1, 2, 10, 12, 13, 15, 16, 17, 19, 24  …  83, 92, 93, 98, 101, 103, 104, 105, 107, 108]]
 [[2, 12, 13, 14, 16, 21, 23, 24, 25, 27  …  88, 90, 96, 97, 99, 101, 104, 105, 108, 114], [1, 4, 5, 7, 9, 11, 15, 18, 31, 33  …  86, 87, 91, 93, 94, 100, 103, 106, 110, 115], [3, 6, 8, 10, 17, 19, 20, 22, 26, 28  …  89, 92, 95, 98, 102, 107, 109, 111, 112, 113]]

• (2) Replicated "Test-set" CV

  • Train is split to Cal/Val (e.g. Cal = 70% of Train, Val = 30% of Train), and this is replicated.

#pct = .30
#m = Int(round(pct * ntrain))
#segm = segmts(ntrain, m; rep = 30)

Illustration of segments:

i = 1  
segm[i]      # the K segments of replication 'i'

3-element Vector{Vector{Int64}}:
 [3, 4, 5, 12, 14, 16, 19, 25, 28, 31  …  95, 96, 97, 103, 104, 105, 106, 109, 112, 114]
 [6, 7, 8, 11, 15, 18, 20, 23, 26, 29  …  84, 86, 94, 98, 102, 107, 108, 110, 113, 115]
 [1, 2, 9, 10, 13, 17, 21, 22, 24, 27  …  82, 85, 87, 88, 89, 92, 99, 100, 101, 111]

k = 1
segm[i][k]   # segment 'k' of replication 'i'

39-element Vector{Int64}:
   3
   4
   5
  12
  14
  16
  19
  25
  28
  31
   ⋮
  96
  97
 103
 104
 105
 106
 109
 112
 114

Grid-search

The best syntax to use function gridcv for LV-based functions (eg. plskern, kplsr, lwplsr, etc.) is to set parameter nlv outside its argument pars defining the grid (in general with function mpar). In that case, the computation time is reduced [See the naïve and non-optimized syntax at the end of this script]. This is the same principle when definining the parameter lb in ridge-based functions (rr, krr, etc.).

nlv = 0:20
model = plskern()
rescv = gridcv(model, Xtrain, ytrain; segm, score = rmsep, nlv)
@names rescv 
res = rescv.res
res_rep = rescv.res_rep

plotgrid(res.nlv, res.y1; step = 2, xlabel = "Nb. LVs", ylabel = "RMSEP-CV").f

f, ax = plotgrid(res.nlv, res.y1; step = 2, xlabel = "Nb. LVs", ylabel = "RMSEP-CV")
for i = 1:rep, j = 1:K
    zres = res_rep[res_rep.rep .== i .&& res_rep.segm .== j, :]
    lines!(ax, zres.nlv, zres.y1; color = (:grey, .2))
end
lines!(ax, res.nlv, res.y1; color = :red, linewidth = 1)
f

Selection of the best parameter combination

u = findall(res.y1 .== minimum(res.y1))[1] 
res[u, :]

Final prediction (Test) using the optimal model

model = plskern(nlv = res.nlv[u])
fit!(model, Xtrain, ytrain)
pred = predict(model, Xtest).pred

63×1 Matrix{Float64}:
 27.396108728712
  4.4912779253048605
  4.9900980331906455
 11.016752894644581
 13.6730456055306
 20.679552141109767
 25.814043422530865
 54.06157064233883
  9.87588864713091
  5.04840120243277
  ⋮
 17.006189347070176
 17.53307437947076
 20.679445265823034
 26.951106243297836
 25.25250542344898
 32.719328178913216
 33.196189093807185
 37.01967890654217
 46.24659766456356

Generalization error

rmsep(pred, ytest)

1×1 Matrix{Float64}:
 2.075776908672637

Plotting predictions vs. observed data

plotxy(pred, ytest; size = (500, 400), color = (:red, .5), bisect = true, title = string("Test set - variable ", nam), 
    xlabel = "Prediction", ylabel = "Observed").f

Additional parameters in the grid

If other parameters have to be defined in the grid, they have to be set in argument pars built with function mpar, such as in the example below.

pars = mpar(scal = [false; true])
nlv = 0:20
model = plskern()
res = gridcv(model, Xtrain, ytrain; segm, score = rmsep, pars, nlv).res

plotgrid(res.nlv, res.y1, res.scal; step = 2, xlabel = "Nb. LVs", ylabel = "RMSEP-Val").f

u = findall(res.y1 .== minimum(res.y1))[1] 
res[u, :]

model = plskern(nlv = res.nlv[u], scal = res.scal[u])
fit!(model, Xtrain, ytrain)
pred = predict(model, Xtest).pred

63×1 Matrix{Float64}:
 27.396108728712
  4.4912779253048605
  4.9900980331906455
 11.016752894644581
 13.6730456055306
 20.679552141109767
 25.814043422530865
 54.06157064233883
  9.87588864713091
  5.04840120243277
  ⋮
 17.006189347070176
 17.53307437947076
 20.679445265823034
 26.951106243297836
 25.25250542344898
 32.719328178913216
 33.196189093807185
 37.01967890654217
 46.24659766456356

Naïve syntax to use gridcv for LV-based functions

Parameter nlv can also be set in argument pars (wich is the generic approach to define the grid). This is strictly equivalent (gives the same results) but the computations are slower.

pars = mpar(nlv = 0:20)
model = plskern()
gridcv(model, Xtrain, ytrain; segm, score = rmsep, pars).res

pars = mpar(nlv = 0:20, scal = [false; true])
model = plskern()
gridcv(model, Xtrain, ytrain; segm, score = rmsep, pars).res

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