From 4ffe934690e3cd047b157ed7c223896566f922c9 Mon Sep 17 00:00:00 2001 From: math4mad Date: Sun, 15 Oct 2023 16:03:31 +0800 Subject: [PATCH] add note add Olivetti pca note --- .../2-olivetti-face/execute-results/html.json | 11 + .../3-olivetti-face/execute-results/html.json | 15 + .../figure-html/cell-6-output-1.png | Bin 0 -> 933745 bytes .../2-olivetti-face-intro.qmd | 113 +++++ .../dimension-reduction/3-olivetti-face.qmd | 93 ++++ .../data/scikit_fetch_olivetti_faces.csv.zip | Bin 0 -> 2482194 bytes .../olivetti-face-code/1-dataprocessing.jl | 29 ++ .../olivetti-face-code/2-train&save-model.jl | 39 ++ .../3-transform-reconstruct-methods.jl | 127 ++++++ .../models/of-model-1pcs.jlso | Bin 0 -> 2285149 bytes .../models/of-model-2pcs.jlso | Bin 0 -> 2348039 bytes .../models/of-model-3pcs.jlso | Bin 0 -> 2410995 bytes .../olivetti-face-code/olivetti_faces.csv | 401 ++++++++++++++++++ 13 files changed, 828 insertions(+) create mode 100644 _freeze/category/dimension-reduction/2-olivetti-face/execute-results/html.json create mode 100644 _freeze/category/dimension-reduction/3-olivetti-face/execute-results/html.json create mode 100644 _freeze/category/dimension-reduction/3-olivetti-face/figure-html/cell-6-output-1.png create mode 100644 category/dimension-reduction/2-olivetti-face-intro.qmd create mode 100644 category/dimension-reduction/3-olivetti-face.qmd create mode 100644 category/dimension-reduction/data/scikit_fetch_olivetti_faces.csv.zip create mode 100644 category/dimension-reduction/olivetti-face-code/1-dataprocessing.jl create mode 100644 category/dimension-reduction/olivetti-face-code/2-train&save-model.jl create mode 100644 category/dimension-reduction/olivetti-face-code/3-transform-reconstruct-methods.jl create mode 100644 category/dimension-reduction/olivetti-face-code/models/of-model-1pcs.jlso create mode 100644 category/dimension-reduction/olivetti-face-code/models/of-model-2pcs.jlso create mode 100644 category/dimension-reduction/olivetti-face-code/models/of-model-3pcs.jlso create mode 100644 category/dimension-reduction/olivetti-face-code/olivetti_faces.csv diff --git a/_freeze/category/dimension-reduction/2-olivetti-face/execute-results/html.json b/_freeze/category/dimension-reduction/2-olivetti-face/execute-results/html.json new file mode 100644 index 0000000..7103385 --- /dev/null +++ b/_freeze/category/dimension-reduction/2-olivetti-face/execute-results/html.json @@ -0,0 +1,11 @@ +{ + "hash": "b3e9fbdefd68e585282e16606555fadb", + "result": { + "markdown": "---\ntitle: \"2-olivetti-face-project\"\ncode-fold: true\n---\n\n## 1. load package\n\n::: {.cell execution_count=1}\n``` {.julia .cell-code}\nusing MLJ,DataFrames,CSV,Random\ninclude(\"./olivetti-face-code/1-dataprocessing.jl\")\n```\n\n::: {.cell-output .cell-output-display execution_count=2}\n```\nload_olivetti_faces (generic function with 1 method)\n```\n:::\n:::\n\n\n## 2. load data\n\n::: {.cell execution_count=2}\n``` {.julia .cell-code}\n(Xtrain, Xtest), (ytrain, ytest)=load_olivetti_faces()\n```\n\n::: {.cell-output .cell-output-display execution_count=3}\n\n::: {.ansi-escaped-output}\n```{=html}\n
((320×4096 DataFrame\n Row  x1         x2        x3         x4        x5        x6        x7        ⋯\n     │ Float64    Float64   Float64    Float64   Float64   Float64   Float64   ⋯\n─────┼──────────────────────────────────────────────────────────────────────────\n   1 │ 0.595041   0.640496  0.615702   0.644628  0.68595   0.72314   0.731405  ⋯\n   2 │ 0.103306   0.219008  0.177686   0.219008  0.392562  0.57438   0.669422\n   3 │ 0.289256   0.338843  0.417355   0.504132  0.553719  0.561983  0.582645\n   4 │ 0.528926   0.797521  0.826446   0.822314  0.822314  0.818182  0.805785\n   5 │ 0.161157   0.202479  0.268595   0.334711  0.384298  0.392562  0.396694  ⋯\n   6 │ 0.169422   0.293388  0.561983   0.677686  0.727273  0.756198  0.768595\n   7 │ 0.136364   0.177686  0.235537   0.289256  0.334711  0.363636  0.396694\n   8 │ 0.768595   0.756198  0.743802   0.743802  0.752066  0.747934  0.735537\n   9 │ 0.144628   0.210744  0.285124   0.342975  0.392562  0.404959  0.409091  ⋯\n  10 │ 0.566116   0.595041  0.603306   0.619835  0.636364  0.640496  0.661157\n  11 │ 0.524793   0.53719   0.578512   0.628099  0.669422  0.690083  0.68595\n  ⋮  │     ⋮         ⋮          ⋮         ⋮         ⋮         ⋮         ⋮      ⋱\n 311 │ 0.181818   0.338843  0.355372   0.404959  0.438017  0.458678  0.471074\n 312 │ 0.454545   0.528926  0.644628   0.747934  0.780992  0.780992  0.801653  ⋯\n 313 │ 0.553719   0.607438  0.636364   0.64876   0.652893  0.64876   0.673554\n 314 │ 0.0909091  0.136364  0.177686   0.231405  0.363636  0.504132  0.541322\n 315 │ 0.252066   0.252066  0.252066   0.252066  0.256198  0.239669  0.235537\n 316 │ 0.326446   0.483471  0.524793   0.599174  0.665289  0.702479  0.702479  ⋯\n 317 │ 0.53719    0.57438   0.553719   0.615702  0.38843   0.487603  0.690083\n 318 │ 0.128099   0.18595   0.247934   0.31405   0.38843   0.46281   0.520661\n 319 │ 0.586777   0.702479  0.731405   0.731405  0.743802  0.772727  0.793388\n 320 │ 0.136364   0.107438  0.0909091  0.115702  0.115702  0.119835  0.181818  ⋯\n                                               4089 columns and 299 rows omitted, 80×4096 DataFrame\n Row  x1        x2        x3        x4        x5        x6        x7        x ⋯\n     │ Float64   Float64   Float64   Float64   Float64   Float64   Float64   F ⋯\n─────┼──────────────────────────────────────────────────────────────────────────\n   1 │ 0.219008  0.235537  0.252066  0.326446  0.392562  0.553719  0.714876  0 ⋯\n   2 │ 0.194215  0.268595  0.367769  0.487603  0.545455  0.561983  0.578512  0\n   3 │ 0.210744  0.206612  0.194215  0.181818  0.219008  0.239669  0.256198  0\n   4 │ 0.392562  0.475207  0.661157  0.590909  0.471074  0.545455  0.673554  0\n   5 │ 0.719008  0.727273  0.72314   0.714876  0.72314   0.731405  0.739669  0 ⋯\n   6 │ 0.429752  0.458678  0.549587  0.623967  0.673554  0.714876  0.72314   0\n   7 │ 0.289256  0.157025  0.14876   0.190083  0.169422  0.194215  0.404959  0\n   8 │ 0.628099  0.665289  0.68595   0.694215  0.719008  0.731405  0.752066  0\n   9 │ 0.479339  0.549587  0.628099  0.690083  0.677686  0.652893  0.640496  0 ⋯\n  10 │ 0.677686  0.677686  0.681818  0.706612  0.731405  0.739669  0.756198  0\n  11 │ 0.123967  0.132231  0.11157   0.11157   0.119835  0.136364  0.136364  0\n  ⋮  │    ⋮         ⋮         ⋮         ⋮         ⋮         ⋮         ⋮        ⋱\n  71 │ 0.123967  0.128099  0.115702  0.136364  0.115702  0.107438  0.115702  0\n  72 │ 0.384298  0.22314   0.22314   0.305785  0.429752  0.508265  0.557851  0 ⋯\n  73 │ 0.409091  0.590909  0.657025  0.681818  0.694215  0.731405  0.760331  0\n  74 │ 0.487603  0.330578  0.252066  0.38843   0.785124  0.789256  0.780992  0\n  75 │ 0.285124  0.285124  0.272727  0.214876  0.169422  0.165289  0.264463  0\n  76 │ 0.136364  0.132231  0.123967  0.119835  0.11157   0.128099  0.132231  0 ⋯\n  77 │ 0.243802  0.243802  0.247934  0.247934  0.252066  0.256198  0.256198  0\n  78 │ 0.603306  0.586777  0.541322  0.603306  0.603306  0.607438  0.64876   0\n  79 │ 0.772727  0.764463  0.752066  0.764463  0.785124  0.793388  0.797521  0\n  80 │ 0.475207  0.491736  0.5       0.512397  0.524793  0.528926  0.545455  0 ⋯\n                                                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]))
\n```\n:::\n\n:::\n:::\n\n\n", + "supporting": [ + "2-olivetti-face_files/figure-html" + ], + "filters": [], + "includes": {} + } +} \ No newline at end of file diff --git a/_freeze/category/dimension-reduction/3-olivetti-face/execute-results/html.json b/_freeze/category/dimension-reduction/3-olivetti-face/execute-results/html.json new file mode 100644 index 0000000..e092d93 --- /dev/null +++ b/_freeze/category/dimension-reduction/3-olivetti-face/execute-results/html.json @@ -0,0 +1,15 @@ +{ + "hash": "d85af18cb48cb03da5f06316a53d0f6a", + "result": { + "markdown": "---\ntitle: \"3-olivetti-face\"\nauthor: math4mad\ncode-fold: true\n---\n\n## 1. load package\n\n::: {.cell execution_count=1}\n``` {.julia .cell-code}\ninclude(\"./olivetti-face-code/1-dataprocessing.jl\")\n\nimport MLJ: transform, inverse_transform\nusing MLJ,DataFrames,CSV,Random,JLSO,GLMakie\nRandom.seed!(4545343)\n```\n\n::: {.cell-output .cell-output-display execution_count=26}\n```\nTaskLocalRNG()\n```\n:::\n:::\n\n\n## 2. import data\n\n::: {.cell execution_count=2}\n``` {.julia .cell-code}\n(Xtrain, Xtest), (ytrain, ytest)=load_olivetti_faces()\n```\n\n::: {.cell-output .cell-output-display execution_count=27}\n\n::: {.ansi-escaped-output}\n```{=html}\n
((320×4096 DataFrame\n Row  x1         x2        x3         x4        x5        x6        x7        ⋯\n     │ Float64    Float64   Float64    Float64   Float64   Float64   Float64   ⋯\n─────┼──────────────────────────────────────────────────────────────────────────\n   1 │ 0.595041   0.640496  0.615702   0.644628  0.68595   0.72314   0.731405  ⋯\n   2 │ 0.103306   0.219008  0.177686   0.219008  0.392562  0.57438   0.669422\n   3 │ 0.289256   0.338843  0.417355   0.504132  0.553719  0.561983  0.582645\n   4 │ 0.528926   0.797521  0.826446   0.822314  0.822314  0.818182  0.805785\n   5 │ 0.161157   0.202479  0.268595   0.334711  0.384298  0.392562  0.396694  ⋯\n   6 │ 0.169422   0.293388  0.561983   0.677686  0.727273  0.756198  0.768595\n   7 │ 0.136364   0.177686  0.235537   0.289256  0.334711  0.363636  0.396694\n   8 │ 0.768595   0.756198  0.743802   0.743802  0.752066  0.747934  0.735537\n   9 │ 0.144628   0.210744  0.285124   0.342975  0.392562  0.404959  0.409091  ⋯\n  10 │ 0.566116   0.595041  0.603306   0.619835  0.636364  0.640496  0.661157\n  11 │ 0.524793   0.53719   0.578512   0.628099  0.669422  0.690083  0.68595\n  ⋮  │     ⋮         ⋮          ⋮         ⋮         ⋮         ⋮         ⋮      ⋱\n 311 │ 0.181818   0.338843  0.355372   0.404959  0.438017  0.458678  0.471074\n 312 │ 0.454545   0.528926  0.644628   0.747934  0.780992  0.780992  0.801653  ⋯\n 313 │ 0.553719   0.607438  0.636364   0.64876   0.652893  0.64876   0.673554\n 314 │ 0.0909091  0.136364  0.177686   0.231405  0.363636  0.504132  0.541322\n 315 │ 0.252066   0.252066  0.252066   0.252066  0.256198  0.239669  0.235537\n 316 │ 0.326446   0.483471  0.524793   0.599174  0.665289  0.702479  0.702479  ⋯\n 317 │ 0.53719    0.57438   0.553719   0.615702  0.38843   0.487603  0.690083\n 318 │ 0.128099   0.18595   0.247934   0.31405   0.38843   0.46281   0.520661\n 319 │ 0.586777   0.702479  0.731405   0.731405  0.743802  0.772727  0.793388\n 320 │ 0.136364   0.107438  0.0909091  0.115702  0.115702  0.119835  0.181818  ⋯\n                                               4089 columns and 299 rows omitted, 80×4096 DataFrame\n Row  x1        x2        x3        x4        x5        x6        x7        x ⋯\n     │ Float64   Float64   Float64   Float64   Float64   Float64   Float64   F ⋯\n─────┼──────────────────────────────────────────────────────────────────────────\n   1 │ 0.219008  0.235537  0.252066  0.326446  0.392562  0.553719  0.714876  0 ⋯\n   2 │ 0.194215  0.268595  0.367769  0.487603  0.545455  0.561983  0.578512  0\n   3 │ 0.210744  0.206612  0.194215  0.181818  0.219008  0.239669  0.256198  0\n   4 │ 0.392562  0.475207  0.661157  0.590909  0.471074  0.545455  0.673554  0\n   5 │ 0.719008  0.727273  0.72314   0.714876  0.72314   0.731405  0.739669  0 ⋯\n   6 │ 0.429752  0.458678  0.549587  0.623967  0.673554  0.714876  0.72314   0\n   7 │ 0.289256  0.157025  0.14876   0.190083  0.169422  0.194215  0.404959  0\n   8 │ 0.628099  0.665289  0.68595   0.694215  0.719008  0.731405  0.752066  0\n   9 │ 0.479339  0.549587  0.628099  0.690083  0.677686  0.652893  0.640496  0 ⋯\n  10 │ 0.677686  0.677686  0.681818  0.706612  0.731405  0.739669  0.756198  0\n  11 │ 0.123967  0.132231  0.11157   0.11157   0.119835  0.136364  0.136364  0\n  ⋮  │    ⋮         ⋮         ⋮         ⋮         ⋮         ⋮         ⋮        ⋱\n  71 │ 0.123967  0.128099  0.115702  0.136364  0.115702  0.107438  0.115702  0\n  72 │ 0.384298  0.22314   0.22314   0.305785  0.429752  0.508265  0.557851  0 ⋯\n  73 │ 0.409091  0.590909  0.657025  0.681818  0.694215  0.731405  0.760331  0\n  74 │ 0.487603  0.330578  0.252066  0.38843   0.785124  0.789256  0.780992  0\n  75 │ 0.285124  0.285124  0.272727  0.214876  0.169422  0.165289  0.264463  0\n  76 │ 0.136364  0.132231  0.123967  0.119835  0.11157   0.128099  0.132231  0 ⋯\n  77 │ 0.243802  0.243802  0.247934  0.247934  0.252066  0.256198  0.256198  0\n  78 │ 0.603306  0.586777  0.541322  0.603306  0.603306  0.607438  0.64876   0\n  79 │ 0.772727  0.764463  0.752066  0.764463  0.785124  0.793388  0.797521  0\n  80 │ 0.475207  0.491736  0.5       0.512397  0.524793  0.528926  0.545455  0 ⋯\n                                                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]))
\n```\n:::\n\n:::\n:::\n\n\n## 3. train&save model\n\n::: {.cell execution_count=3}\n``` {.julia .cell-code}\n PCA = @load PCA pkg=MultivariateStats\n function make_model(Xtr)\n return (dim)->begin\n model = PCA(maxoutdim=dim)\n mach = machine(model, Xtr) |> fit!\n try\n JLSO.save(\"./olivetti-face-code/models/of-model-$(dim)pcs.jlso\",:pca=>mach)\n @info \"$(dim) dimension pca model saved\"\n catch e\n @warn \"$(e) has problem\"\n end\n end\nend\nmake_ol_model=make_model(Xtrain)\nmake_ol_model.([1,2,3,100])\n```\n\n::: {.cell-output .cell-output-stdout}\n```\nimport MLJMultivariateStatsInterface ✔\n```\n:::\n\n::: {.cell-output .cell-output-stderr}\n```\n[ Info: For silent loading, specify `verbosity=0`. \n[ Info: Training machine(PCA(maxoutdim = 1, …), …).\n[ Info: 1 dimension pca model saved\n[ Info: Training machine(PCA(maxoutdim = 2, …), …).\n[ Info: 2 dimension pca model saved\n[ Info: Training machine(PCA(maxoutdim = 3, …), …).\n[ Info: 3 dimension pca model saved\n[ Info: Training machine(PCA(maxoutdim = 100, …), …).\n[ Info: 100 dimension pca model saved\n```\n:::\n\n::: {.cell-output .cell-output-display execution_count=28}\n```\n4-element Vector{Nothing}:\n nothing\n nothing\n nothing\n nothing\n```\n:::\n:::\n\n\n## 4. imgs project to low dimension feature space\n第三行是降维到 100的图片, 最后一行是原始图片\n\n::: {.cell execution_count=4}\n``` {.julia .cell-code}\ninclude(\"./olivetti-face-code/3-transform-reconstruct-methods.jl\")\ncat=ytrain|>Array|>levels\nrows,cols=size(Xtrain)\n\npick20=rand(1:rows,20)\npickXtrain=Xtrain[pick20,:]\npickytrain=ytrain[pick20]\n\npcaData=transform_to_2d(pickXtrain)\nreconstructImgs=reconstruct_data(pcaData)\n\npcaData3=transform_to_3d(pickXtrain)\nreconstructImgs3=reconstruct_data(pcaData3)\n\ntransform_to_100d=transform_to_pcadata1(100)\npcaData100=transform_to_100d(pickXtrain)\nreconstructImgs100=reconstruct_data(pcaData100)\n\ndf=vcat(reconstructImgs,reconstructImgs3,reconstructImgs100,pickXtrain)\n```\n\n::: {.cell-output .cell-output-stderr}\n```\n[ Info: 2 pca proceeding...\n[ Info: imgs reconstructing from 2 dimension\n[ Info: 3 pca proceeding...\n[ Info: imgs reconstructing from 3 dimension\n[ Info: 100 pca proceeding...\n[ Info: imgs reconstructing from 100 dimension\n```\n:::\n\n::: {.cell-output .cell-output-display execution_count=29}\n```{=html}\n
80×4096 DataFrame
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Rowx1x2x3x4x5x6x7x8x9x10x11x12x13x14x15x16x17x18x19x20x21x22x23x24x25x26x27x28x29x30x31x32x33x34x35x36x37x38x39x40x41x42x43x44x45x46x47x48x49x50x51x52x53x54x55x56x57x58x59x60x61x62x63x64x65x66x67x68x69x70x71x72x73x74x75x76x77x78x79x80x81x82x83x84x85x86x87x88x89x90x91x92x93x94x95x96x97x98x99x100
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\n```\n:::\n:::\n\n\nplot reconstruct imgs\n\n::: {.cell execution_count=5}\n``` {.julia .cell-code}\nfunction plot_img(df)\n \n fig=Figure(resolution=(130*20,130*4))\n \n for i in 0:3\n for j in 1:20\n idx=i*20+j\n ax=Axis(fig[i+1,j],yreversed=true)\n img=df[idx,:]|>Array|>d->reshape(d,w,h)\n image!(ax,img)\n hidespines!(ax)\n hidedecorations!(ax)\n end\n end\n\n fig\n #save(\"./imgs/reconstruct-of-face.png\",fig)\nend\n\n\nplot_img(df)\n```\n\n::: {.cell-output .cell-output-display execution_count=30}\n![](3-olivetti-face_files/figure-html/cell-6-output-1.png){}\n:::\n:::\n\n\n", + "supporting": [ + "3-olivetti-face_files" + ], + "filters": [], + "includes": { + "include-in-header": [ + "\n\n\n" + ] + } + } +} \ No newline at end of file diff --git a/_freeze/category/dimension-reduction/3-olivetti-face/figure-html/cell-6-output-1.png b/_freeze/category/dimension-reduction/3-olivetti-face/figure-html/cell-6-output-1.png new file mode 100644 index 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zUyJ@OCySl^POlC`J>OY+x_R3F;|FwJ1xxfFE zKdiq={M%14M6UkeZ$HHOsNeNjfBz}3EdM`#nA%*wn1vtBKYDPSioY~t#(SoZp1Jz} E0wr$7`v3p{ literal 0 HcmV?d00001 diff --git a/category/dimension-reduction/2-olivetti-face-intro.qmd b/category/dimension-reduction/2-olivetti-face-intro.qmd new file mode 100644 index 0000000..dafb587 --- /dev/null +++ b/category/dimension-reduction/2-olivetti-face-intro.qmd @@ -0,0 +1,113 @@ +--- +title: "2-olivetti-face-project" +code-fold: true +--- + + +## olivetti face recognition 的项目 + +## 1. dataprocessing.jl + +读取 csv 文件, 返回 返回 olivetti face 训练数据,测试数据和标签 +`load_olivetti_faces` 函数返回训练,测试数据集和对应标签 +```julia +(Xtrain, Xtest), (ytrain, ytest)=load_olivetti_faces() +``` + +## 2. train&save-model.jl + +导入数据,返回一个高阶函数`make_model` +`make_model` 函数首先接受训练数据集, 然后等待 `dim` 需要缩减到的维度参数 + +最终训练的模型保存的对应的 jlso 文件中:`JLSO.save("$(pwd())/models/of-model-$(dim)pcs.jlso",:pca=>mach)` + + +```julia +function make_model(Xtr) + return (dim)->begin + model = PCA(maxoutdim=dim) + mach = machine(model, Xtr) |> fit! + try + JLSO.save("$(pwd())/models/of-model-$(dim)pcs.jlso",:pca=>mach) + @info "$(dim) dimension pca model saved" + catch e + @warn "$(e) has problem" + end + end +end + +make_ol_model=make_model(Xtrain) +#make_ol_model.([10,20,150]) +``` + + +## 3-transform-reconstruct-methods +在 `MLJ.jl`的 pca 方法和 `MultiVariate.jl` 的方法稍有不同, 使用的是 `transform`函数,而不是`project`方法 + +主要函数为`transform_to_pcadata1`,为高阶函数 +输入参数`dim` 为需要缩减到的维度 +函数内部调用第2 步获得的模型 +返回一个函数 等待 df 参数 +内部调用`transform`函数对数据做降维处理 +返回数据 + +```julia +function transform_to_pcadata(dim::Int) + mach = JLSO.load("$(pwd())/models/of-model-$(dim)pcs.jlso")[:pca] + return (imgs::DataFrame)->begin + @info "$dim pca proceeding..." + pcaX = transform(mach, imgs) # 降维数据 + # 返回降维数据 + return pcaX + end +end +``` + + +`transform_to_pcadata2` 与`transform_to_pcadata1` 一样是高阶函数 +但是参数输入的顺序不同, `transform_to_pcadata2`中先输入dataframe, 然后等待维度参数 +```julia + function transform_to_pcadata2(imgs::DataFrame) + + return (dim::Int)->begin + @info "$dim pca proceeding..." + mach = JLSO.load("$(pwd())/models/of-model-$(dim)pcs.jlso")[:pca] + pcaX = transform(mach, imgs) # 降维数据 + # 返回降维数据 + return pcaX + end + end +``` + +### 重建数据方法 +从低维度数据恢复原始维度数据, 在`MLJ`中使用的方法是`inverse_transform` +`reconstruct_data` 方法首先从数据 dataframe 中获取维度`column`数据 +从存储模型中调用训练模型 +执行重建变换 + +```julia + """ + reconstruct_data(imgs::DataFrame) + 从降维数据重建图片 + TBW + """ + function reconstruct_data(imgs::DataFrame) + + _,cols=size(imgs) + @info "imgs reconstructing from $(cols) dimension" + mach = JLSO.load("$(pwd())/models/of-model-$(cols)pcs.jlso")[:pca] + Xr = inverse_transform(mach, imgs) # 重建近似数据 + return Xr + end +``` + +## 维度缩减到 1d 的数据 + +以 faces 为例,如果缩减到一个维度, 获得的数据是所有图片共用的最大元素,也就是所有人面部共用的特征 + + + + + + + diff --git a/category/dimension-reduction/3-olivetti-face.qmd b/category/dimension-reduction/3-olivetti-face.qmd new file mode 100644 index 0000000..17b7bf9 --- /dev/null +++ b/category/dimension-reduction/3-olivetti-face.qmd @@ -0,0 +1,93 @@ +--- +title: "3-olivetti-face" +author: math4mad +code-fold: true +--- + +## 1. load package +```{julia} +include("./olivetti-face-code/1-dataprocessing.jl") + +import MLJ: transform, inverse_transform +using MLJ,DataFrames,CSV,Random,JLSO,GLMakie +Random.seed!(4545343) + +``` + +## 2. import data +```{julia} +(Xtrain, Xtest), (ytrain, ytest)=load_olivetti_faces() +``` + +## 3. train&save model +```{julia} + PCA = @load PCA pkg=MultivariateStats + function make_model(Xtr) + return (dim)->begin + model = PCA(maxoutdim=dim) + mach = machine(model, Xtr) |> fit! + try + JLSO.save("./olivetti-face-code/models/of-model-$(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]) + +``` + +## 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 +```{julia} +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) +``` +plot reconstruct imgs +```{julia} +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) +``` + + + + + diff --git a/category/dimension-reduction/data/scikit_fetch_olivetti_faces.csv.zip 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