3-olivetti-face

Author

math4mad

1. load package

Code

include("./olivetti-face-code/1-dataprocessing.jl")

import MLJ: transform, inverse_transform
using MLJ,DataFrames,CSV,Random,JLSO,GLMakie
Random.seed!(4545343)

TaskLocalRNG()

2. import data

Code

(Xtrain, Xtest), (ytrain, ytest)=load_olivetti_faces()

((320×4096 DataFrame
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     │ Float64    Float64   Float64    Float64   Float64   Float64   Float64   ⋯
─────┼──────────────────────────────────────────────────────────────────────────
   1 │ 0.595041   0.640496  0.615702   0.644628  0.68595   0.72314   0.731405  ⋯
   2 │ 0.103306   0.219008  0.177686   0.219008  0.392562  0.57438   0.669422
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   8 │ 0.768595   0.756198  0.743802   0.743802  0.752066  0.747934  0.735537
   9 │ 0.144628   0.210744  0.285124   0.342975  0.392562  0.404959  0.409091  ⋯
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  ⋮  │     ⋮         ⋮          ⋮         ⋮         ⋮         ⋮         ⋮      ⋱
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                                               4089 columns and 299 rows omitted, 80×4096 DataFrame
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     │ Float64   Float64   Float64   Float64   Float64   Float64   Float64   F ⋯
─────┼──────────────────────────────────────────────────────────────────────────
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   5 │ 0.719008  0.727273  0.72314   0.714876  0.72314   0.731405  0.739669  0 ⋯
   6 │ 0.429752  0.458678  0.549587  0.623967  0.673554  0.714876  0.72314   0
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   8 │ 0.628099  0.665289  0.68595   0.694215  0.719008  0.731405  0.752066  0
   9 │ 0.479339  0.549587  0.628099  0.690083  0.677686  0.652893  0.640496  0 ⋯
  10 │ 0.677686  0.677686  0.681818  0.706612  0.731405  0.739669  0.756198  0
  11 │ 0.123967  0.132231  0.11157   0.11157   0.119835  0.136364  0.136364  0
  ⋮  │    ⋮         ⋮         ⋮         ⋮         ⋮         ⋮         ⋮        ⋱
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                                                4089 columns and 59 rows omitted), (CategoricalArrays.CategoricalValue{Int64, UInt32}[27, 3, 39, 10, 23, 24, 23, 30, 23, 14  …  21, 26, 5, 35, 28, 9, 31, 0, 15, 37], CategoricalArrays.CategoricalValue{Int64, UInt32}[6, 35, 6, 31, 5, 4, 3, 2, 19, 4  …  32, 29, 22, 36, 12, 32, 16, 14, 3, 20]))

3. train&save model

Code

    PCA = @load PCA pkg=MultivariateStats
    function  make_model(Xtr)
    return (dim)->begin
        model = PCA(maxoutdim=dim)
        mach = machine(model, Xtr) |> fit!
        try
            JLSO.save("./olivetti-face-code/models/of-model-$(dim)pcs.jlso",:pca=>mach)
            @info "$(dim) dimension pca model saved"
        catch e
            @warn "$(e) has problem"
        end
    end
end
make_ol_model=make_model(Xtrain)
make_ol_model.([1,2,3,100])

import MLJMultivariateStatsInterface ✔

[ Info: For silent loading, specify `verbosity=0`. 
[ Info: Training machine(PCA(maxoutdim = 1, …), …).
[ Info: 1 dimension pca model saved
[ Info: Training machine(PCA(maxoutdim = 2, …), …).
[ Info: 2 dimension pca model saved
[ Info: Training machine(PCA(maxoutdim = 3, …), …).
[ Info: 3 dimension pca model saved
[ Info: Training machine(PCA(maxoutdim = 100, …), …).
[ Info: 100 dimension pca model saved

4-element Vector{Nothing}:
 nothing
 nothing
 nothing
 nothing

4. imgs project to low dimension feature space

第三行是降维到 100的图片, 最后一行是原始图片

Code

include("./olivetti-face-code/3-transform-reconstruct-methods.jl")
cat=ytrain|>Array|>levels
rows,cols=size(Xtrain)

pick20=rand(1:rows,20)
pickXtrain=Xtrain[pick20,:]
pickytrain=ytrain[pick20]

pcaData=transform_to_2d(pickXtrain)
reconstructImgs=reconstruct_data(pcaData)

pcaData3=transform_to_3d(pickXtrain)
reconstructImgs3=reconstruct_data(pcaData3)

transform_to_100d=transform_to_pcadata1(100)
pcaData100=transform_to_100d(pickXtrain)
reconstructImgs100=reconstruct_data(pcaData100)

df=vcat(reconstructImgs,reconstructImgs3,reconstructImgs100,pickXtrain)

[ Info: 2 pca proceeding...
[ Info: imgs  reconstructing from 2 dimension
[ Info: 3 pca proceeding...
[ Info: imgs  reconstructing from 3 dimension
[ Info: 100 pca proceeding...
[ Info: imgs  reconstructing from 100 dimension

80×4096 DataFrame

3996 columns and 55 rows omitted

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plot reconstruct imgs

Code

function  plot_img(df)
    
    fig=Figure(resolution=(130*20,130*4))
    
    for i in 0:3
        for j in 1:20
            idx=i*20+j
            ax=Axis(fig[i+1,j],yreversed=true)
            img=df[idx,:]|>Array|>d->reshape(d,w,h)
            image!(ax,img)
            hidespines!(ax)
            hidedecorations!(ax)
        end
    end

    fig
    #save("./imgs/reconstruct-of-face.png",fig)
end


plot_img(df)