ModelSanitizer

Usage

ModelSanitizer exports the sanitize! function and the Model, Data, and ForceSanitize structs.

If your model is stored in m and your data are stored in x1, x2, x3, etc. then you can sanitize your model with:

sanitize!(Model(m), Data(x1), Data(x2), Data(x3), ...)

This will recursively search inside the model m for anything that resembles your data and will delete the data that it finds.

If you happen to know exactly where inside a model the data are stored, you can explicitly tell ModelSanitizer to delete those data. If your model is stored in m, and you know that the fields m.x1, m.x2, m.x3, etc. contain data that needs to be removed, you can force ModelSanitizer to delete those data with:

sanitize!(ForceSanitize(m.x1), ForceSanitize(m.x2), ForceSanitize(m.x3), ...)

Example

julia> using ModelSanitizer

julia> using Statistics

julia> using Test

julia> mutable struct LinearModel{T}
           X::Matrix{T}
           y::Vector{T}
           beta::Vector{T}
           function LinearModel{T}()::LinearModel{T} where T
               m::LinearModel{T} = new()
               return m
           end
       end

julia> function fit!(m::LinearModel{T}, X::Matrix{T}, y::Vector{T})::LinearModel{T} where T
           m.X = deepcopy(X)
           m.y = deepcopy(y)
           m.beta = beta = (m.X'm.X)\(m.X'm.y)
           return m
       end
fit! (generic function with 1 method)

julia> function predict(m::LinearModel{T}, X::Matrix{T})::Vector{T} where T
           y_hat::Vector{T} = X * m.beta
           return y_hat
       end
predict (generic function with 1 method)

julia> function predict(m::LinearModel{T})::Vector{T} where T
           X::Matrix{T} = m.X
           y_hat::Vector{T} = predict(m, X)
           return y_hat
       end
predict (generic function with 2 methods)

julia> function mse(y::Vector{T}, y_hat::Vector{T})::T where T
           _mse::T = mean((y .- y_hat).^2)
           return _mse
       end
mse (generic function with 1 method)

julia> function mse(m::LinearModel{T}, X::Matrix{T}, y::Vector{T})::T where T
           y_hat::Vector{T} = predict(m, X)
           _mse::T = mse(y, y_hat)
           return _mse
       end
mse (generic function with 2 methods)

julia> function mse(m::LinearModel{T})::T where T
           X::Matrix{T} = m.X
           y::Vector{T} = m.y
           _mse::T = mse(m, X, y)
           return _mse
       end
mse (generic function with 3 methods)

julia> rmse(varargs...) = sqrt(mse(varargs...))
rmse (generic function with 1 method)

julia> function r2(y::Vector{T}, y_hat::Vector{T})::T where T
           y_bar::T = mean(y)
           SS_tot::T = sum((y .- y_bar).^2)
           SS_res::T = sum((y .- y_hat).^2)
           _r2::T = 1 - SS_res/SS_tot
           return _r2
       end
r2 (generic function with 1 method)

julia> function r2(m::LinearModel{T}, X::Matrix{T}, y::Vector{T})::T where T
           y_hat::Vector{T} = predict(m, X)
           _r2::T = r2(y, y_hat)
           return _r2
       end
r2 (generic function with 2 methods)

julia> function r2(m::LinearModel{T})::T where T
           X::Matrix{T} = m.X
           y::Vector{T} = m.y
           _r2::T = r2(m, X, y)
           return _r2
       end
r2 (generic function with 3 methods)

julia> X = randn(Float64, 5_000, 14)
5000×14 Array{Float64,2}:
  0.0956436    0.481324   -0.796437  …  -2.26483     1.57243    -1.65105
 -0.306527    -0.880146   -0.764714     -0.182449   -0.0767462  -0.939232
 -0.223116    -0.408068    0.728855      0.220045    0.785533    0.49013
 -0.336363     1.46187    -1.17633      -0.955872    0.699277    0.587961
  0.628275     0.208697   -0.522714      0.116233    0.47314     0.435968
 -0.12303     -0.964061    0.919518  …  -0.0230613  -1.12379    -0.439892
  1.06664      0.96542    -0.250164     -0.776266    1.70851    -1.08608
  0.957151     0.850486    1.31718       0.497219    1.01069    -0.558217
 -0.206168    -0.608305   -0.864631      0.969031    0.209796    1.28718
 -0.658039     1.20687     1.33288       1.54847     0.546286   -1.00404
 -0.598782    -0.193289    0.673134  …  -1.59742     0.410881   -1.61342
  0.31442      0.0199012   0.50533       1.0889     -0.0713841  -1.29933
  0.236585    -1.09804     0.945631     -0.729247   -1.10004    -0.339332
  0.122913     0.619345   -2.90947       1.09613    -0.662693   -1.03469
  1.52615      0.942471    0.262139      0.223064    0.665103    1.4081
 -0.474543     1.9466     -0.408505  …   1.01626    -0.297397   -0.0953909
  0.73664     -0.0796424  -1.84864       1.15935     0.0164378   1.32191
  0.24588      0.271068   -0.238212      0.596475    1.52617    -0.747777
  ⋮                                  ⋱
 -1.07141      0.194049   -0.350011     -0.666195    0.481406   -0.451329
 -0.00993413   0.33006    -0.985443     -0.0395822   2.36983    -0.793007
  0.610014    -0.509744   -1.06447   …   1.19769     1.129       0.397217
  0.785654    -0.361031    0.314127      0.192215    0.789262    0.725731
  0.258588    -2.06379     0.511611      0.0963516  -1.01919    -0.540021
  0.48671     -0.918205    0.264124      0.989929    2.45245    -1.39545
 -1.27085     -0.0617834   2.59491       0.291602    1.28642     0.236496
  1.4044      -1.24472    -0.205029  …   1.99366    -1.58951     0.963728
 -1.07691      0.44178    -0.602841      0.584759   -0.887116    1.36514
  1.13586      0.954756    0.44016      -2.21191    -1.14086    -0.585916
 -0.763031    -1.13348    -1.46696      -1.4121     -0.977694   -0.618883
  0.875367    -1.30925     0.183117      0.224709    0.0752964  -0.92173
  0.659502     0.71971    -1.05538   …  -0.912277   -0.736332    1.01404
 -0.809941     2.02362     1.29668       0.113623   -0.858281    0.0863472
 -1.6409       0.310551   -0.235102     -1.11232    -0.170224    0.404804
 -0.367908    -1.9062      0.245953     -0.751821   -0.794633    0.00894607
  0.380897     2.30871    -0.669909      0.282513   -0.114725   -0.253537

julia> y = X * randn(Float64, 14) + randn(5_000)
5000-element Array{Float64,1}:
 -4.418867382994752
  1.0721553534178543
  2.210545604666476
 -2.5053994409702094
  2.24399399066432
  0.5993702994926247
  2.2040361967638322
 -2.4902628750358193
  4.184644001244288
  1.7688752332135804
 -4.831550352023476
 -1.068149084362266
 -0.746260929030723
  0.032933800577055417
  2.878202216460962
  2.773804353610833
  1.0288912118472482
  3.7799578982964963
  ⋮
  3.1797791441997822
  5.830717537973503
 -0.8191545280972992
  4.649281267724443
  0.9470989605451162
  5.733118456044454
  3.057352206232011
  4.791267454465988
 -4.604222639675081
 -5.755448165821573
 -0.9804279159155482
  2.2904285226467276
  2.809999802793834
  0.7773010780323945
 -2.5205742651574
  3.8866539005621092
 -4.085889556008112

julia> m = LinearModel{Float64}()
LinearModel{Float64}(#undef, #undef, #undef)

julia> testing_rows = 1:2:5_000
1:2:4999

julia> training_rows = setdiff(1:5_000, testing_rows)
2500-element Array{Int64,1}:
    2
    4
    6
    8
   10
   12
   14
   16
   18
   20
   22
   24
   26
   28
   30
   32
   34
   36
    ⋮
 4968
 4970
 4972
 4974
 4976
 4978
 4980
 4982
 4984
 4986
 4988
 4990
 4992
 4994
 4996
 4998
 5000

julia> fit!(m, X[training_rows, :], y[training_rows])
LinearModel{Float64}([-0.306527 -0.880146 … -0.0767462 -0.939232; -0.336363 1.46187 … 0.699277 0.587961; … ; -1.6409 0.310551 … -0.170224 0.404804; 0.380897 2.30871 … -0.114725 -0.253537], [1.07216, -2.5054, 0.59937, -2.49026, 1.76888, -1.06815, 0.0329338, 2.7738, 3.77996, -4.06727  …  2.81088, 3.17978, -0.819155, 0.947099, 3.05735, -4.60422, -0.980428, 2.81, -2.52057, -4.08589], [-0.532213, -1.16489, -0.414974, -0.562536, -0.440432, 0.732505, -1.06754, 0.399485, -0.67281, -1.44599, 0.835625, 0.426459, 1.20088, 0.754435])

julia> @test m.X == X[training_rows, :]
Test Passed

julia> @test m.y == y[training_rows]
Test Passed

julia> @test all(m.X .== X[training_rows, :])
Test Passed

julia> @test all(m.y .== y[training_rows])
Test Passed

julia> @test !all(m.X .== 0)
Test Passed

julia> @test !all(m.y .== 0)
Test Passed

julia> # before sanitization, we can make predictions
       predict(m, X[testing_rows, :])
2500-element Array{Float64,1}:
 -4.513253714187381
  2.5689035333536605
  0.9939782906365846
  1.2513894159362184
  3.2007086601687353
 -5.387968774216589
 -0.1767892797746935
  3.4408813711668165
  0.4625821018811823
  1.649129884116436
 -0.8620887900500149
  0.6504970487658756
  4.287913533796443
 -2.5014166099065136
  1.1666979326633855
  0.2723098985354143
  3.2783930370766634
  2.250636815003683
  ⋮
  1.1999638265752477
  3.8377489399901084
  4.2805489451765935
 -0.5849048693472063
 -0.6574890049656816
  0.2606368302418087
 -4.197310605534758
 -3.5805273324146336
 -0.5244747588662737
  5.274904154193373
  2.7742388165636953
  5.883741172337488
  2.118699747786167
 -4.209943069147431
  2.262361580682631
 -0.5044151513387216
  4.443422779093501

julia> predict(m, X[training_rows, :])
2500-element Array{Float64,1}:
  2.943212508610099
 -0.8226863248850258
  1.031068845178503
 -3.3178919274576053
  0.587046578244962
 -0.032251634503744686
  1.9123819046207888
  3.555603804394087
  2.1728937544760307
 -1.9319447549669504
 -0.7592148524301295
 -7.250437603426189
  4.982277986708986
 -1.8660967909674548
  0.29423182806971415
  0.593840341165224
 -0.26314562641917977
  1.4340414682799685
  ⋮
  1.6038174714835796
  1.3091787016871341
  4.936123830680592
  1.9812183495287048
 -0.848632475032059
  3.1553721781769157
 -5.412240178264108
  1.406559298117795
  3.6433312336276646
  0.3408165307792135
  0.2882242203753349
  1.8120206189755343
 -3.299798877655878
 -0.8793971451160698
  2.3158119962568886
 -2.4598360012327265
 -4.810128269819875

julia> @show mse(m, X[training_rows, :], y[training_rows])
mse(m, X[training_rows, :], y[training_rows]) = 0.9856973993855034
0.9856973993855034

julia> @show rmse(m, X[training_rows, :], y[training_rows])
rmse(m, X[training_rows, :], y[training_rows]) = 0.9928229446308658
0.9928229446308658

julia> @show r2(m, X[training_rows, :], y[training_rows])
r2(m, X[training_rows, :], y[training_rows]) = 0.9044357103305194
0.9044357103305194

julia> @show mse(m, X[testing_rows, :], y[testing_rows])
mse(m, X[testing_rows, :], y[testing_rows]) = 0.9480778102674918
0.9480778102674918

julia> @show rmse(m, X[testing_rows, :], y[testing_rows])
rmse(m, X[testing_rows, :], y[testing_rows]) = 0.9736928726592856
0.9736928726592856

julia> @show r2(m, X[testing_rows, :], y[testing_rows])
r2(m, X[testing_rows, :], y[testing_rows]) = 0.9088387716983182
0.9088387716983182

julia> sanitize!(Model(m), Data(X), Data(y)) # sanitize the model with ModelSanitizer
Model{LinearModel{Float64}}(LinearModel{Float64}([0.0 0.0 … 0.0 0.0; 0.0 0.0 … 0.0 0.0; … ; 0.0 0.0 … 0.0 0.0; 0.0 0.0 … 0.0 0.0], [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0  …  0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0], [-0.532213, -1.16489, -0.414974, -0.562536, -0.440432, 0.732505, -1.06754, 0.399485, -0.67281, -1.44599, 0.835625, 0.426459, 1.20088, 0.754435]))

julia> @test m.X != X[training_rows, :]
Test Passed

julia> @test m.y != y[training_rows]
Test Passed

julia> @test !all(m.X .== X[training_rows, :])
Test Passed

julia> @test !all(m.y .== y[training_rows])
Test Passed

julia> @test all(m.X .== 0)
Test Passed

julia> @test all(m.y .== 0)
Test Passed

julia> # after sanitization, we are still able to make predictions
       predict(m, X[testing_rows, :])
2500-element Array{Float64,1}:
 -4.513253714187381
  2.5689035333536605
  0.9939782906365846
  1.2513894159362184
  3.2007086601687353
 -5.387968774216589
 -0.1767892797746935
  3.4408813711668165
  0.4625821018811823
  1.649129884116436
 -0.8620887900500149
  0.6504970487658756
  4.287913533796443
 -2.5014166099065136
  1.1666979326633855
  0.2723098985354143
  3.2783930370766634
  2.250636815003683
  ⋮
  1.1999638265752477
  3.8377489399901084
  4.2805489451765935
 -0.5849048693472063
 -0.6574890049656816
  0.2606368302418087
 -4.197310605534758
 -3.5805273324146336
 -0.5244747588662737
  5.274904154193373
  2.7742388165636953
  5.883741172337488
  2.118699747786167
 -4.209943069147431
  2.262361580682631
 -0.5044151513387216
  4.443422779093501

julia> predict(m, X[training_rows, :])
2500-element Array{Float64,1}:
  2.943212508610099
 -0.8226863248850258
  1.031068845178503
 -3.3178919274576053
  0.587046578244962
 -0.032251634503744686
  1.9123819046207888
  3.555603804394087
  2.1728937544760307
 -1.9319447549669504
 -0.7592148524301295
 -7.250437603426189
  4.982277986708986
 -1.8660967909674548
  0.29423182806971415
  0.593840341165224
 -0.26314562641917977
  1.4340414682799685
  ⋮
  1.6038174714835796
  1.3091787016871341
  4.936123830680592
  1.9812183495287048
 -0.848632475032059
  3.1553721781769157
 -5.412240178264108
  1.406559298117795
  3.6433312336276646
  0.3408165307792135
  0.2882242203753349
  1.8120206189755343
 -3.299798877655878
 -0.8793971451160698
  2.3158119962568886
 -2.4598360012327265
 -4.810128269819875

julia> @show mse(m, X[training_rows, :], y[training_rows])
mse(m, X[training_rows, :], y[training_rows]) = 0.9856973993855034
0.9856973993855034

julia> @show rmse(m, X[training_rows, :], y[training_rows])
rmse(m, X[training_rows, :], y[training_rows]) = 0.9928229446308658
0.9928229446308658

julia> @show r2(m, X[training_rows, :], y[training_rows])
r2(m, X[training_rows, :], y[training_rows]) = 0.9044357103305194
0.9044357103305194

julia> @show mse(m, X[testing_rows, :], y[testing_rows])
mse(m, X[testing_rows, :], y[testing_rows]) = 0.9480778102674918
0.9480778102674918

julia> @show rmse(m, X[testing_rows, :], y[testing_rows])
rmse(m, X[testing_rows, :], y[testing_rows]) = 0.9736928726592856
0.9736928726592856

julia> @show r2(m, X[testing_rows, :], y[testing_rows])
r2(m, X[testing_rows, :], y[testing_rows]) = 0.9088387716983182
0.9088387716983182

julia> # if you know exactly where the data are stored inside the model, you can
       # directly delete them with ForceSanitize:
       sanitize!(ForceSanitize(m.X), ForceSanitize(m.y))
(ForceSanitize{Array{Float64,2}}([0.0 0.0 … 0.0 0.0; 0.0 0.0 … 0.0 0.0; … ; 0.0 0.0 … 0.0 0.0; 0.0 0.0 … 0.0 0.0]), ForceSanitize{Array{Float64,1}}([0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0  …  0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]))

julia> # we can still make predictions even after using ForceSanitize
       predict(m, X[testing_rows, :])
2500-element Array{Float64,1}:
 -4.513253714187381
  2.5689035333536605
  0.9939782906365846
  1.2513894159362184
  3.2007086601687353
 -5.387968774216589
 -0.1767892797746935
  3.4408813711668165
  0.4625821018811823
  1.649129884116436
 -0.8620887900500149
  0.6504970487658756
  4.287913533796443
 -2.5014166099065136
  1.1666979326633855
  0.2723098985354143
  3.2783930370766634
  2.250636815003683
  ⋮
  1.1999638265752477
  3.8377489399901084
  4.2805489451765935
 -0.5849048693472063
 -0.6574890049656816
  0.2606368302418087
 -4.197310605534758
 -3.5805273324146336
 -0.5244747588662737
  5.274904154193373
  2.7742388165636953
  5.883741172337488
  2.118699747786167
 -4.209943069147431
  2.262361580682631
 -0.5044151513387216
  4.443422779093501

julia> predict(m, X[training_rows, :])
2500-element Array{Float64,1}:
  2.943212508610099
 -0.8226863248850258
  1.031068845178503
 -3.3178919274576053
  0.587046578244962
 -0.032251634503744686
  1.9123819046207888
  3.555603804394087
  2.1728937544760307
 -1.9319447549669504
 -0.7592148524301295
 -7.250437603426189
  4.982277986708986
 -1.8660967909674548
  0.29423182806971415
  0.593840341165224
 -0.26314562641917977
  1.4340414682799685
  ⋮
  1.6038174714835796
  1.3091787016871341
  4.936123830680592
  1.9812183495287048
 -0.848632475032059
  3.1553721781769157
 -5.412240178264108
  1.406559298117795
  3.6433312336276646
  0.3408165307792135
  0.2882242203753349
  1.8120206189755343
 -3.299798877655878
 -0.8793971451160698
  2.3158119962568886
 -2.4598360012327265
 -4.810128269819875

julia> @show mse(m, X[training_rows, :], y[training_rows])
mse(m, X[training_rows, :], y[training_rows]) = 0.9856973993855034
0.9856973993855034

julia> @show rmse(m, X[training_rows, :], y[training_rows])
rmse(m, X[training_rows, :], y[training_rows]) = 0.9928229446308658
0.9928229446308658

julia> @show r2(m, X[training_rows, :], y[training_rows])
r2(m, X[training_rows, :], y[training_rows]) = 0.9044357103305194
0.9044357103305194

julia> @show mse(m, X[testing_rows, :], y[testing_rows])
mse(m, X[testing_rows, :], y[testing_rows]) = 0.9480778102674918
0.9480778102674918

julia> @show rmse(m, X[testing_rows, :], y[testing_rows])
rmse(m, X[testing_rows, :], y[testing_rows]) = 0.9736928726592856
0.9736928726592856

julia> @show r2(m, X[testing_rows, :], y[testing_rows])
r2(m, X[testing_rows, :], y[testing_rows]) = 0.9088387716983182
0.9088387716983182

Name		Name	Last commit message	Last commit date
Latest commit History 332 Commits
.github		.github
benchmark		benchmark
ci/travis		ci/travis
src		src
test		test
.gitignore		.gitignore
.travis.yml		.travis.yml
CODE_OF_CONDUCT.md		CODE_OF_CONDUCT.md
LICENSE		LICENSE
Project.toml		Project.toml
README.md		README.md
bors.toml		bors.toml
ready.yml		ready.yml

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