From b180a29c2d761f85905fb777ae9814ded23566bf Mon Sep 17 00:00:00 2001 From: Mehmet Hakan Satman Date: Sun, 8 Jan 2023 19:26:15 +0300 Subject: [PATCH] v0.9.0 --- CHANGELOG.md | 7 +- Project.toml | 6 +- src/LinRegOutliers.jl | 37 +- src/bch.jl | 58 +-- src/bchplot.jl | 69 +++ src/dataimage.jl | 13 +- src/mveltsplot.jl | 5 +- test/Manifest.toml | 976 ++++++++++++++++++++++++++++++++++++------ test/testdataimage.jl | 1 + test/testga.jl | 1 - test/testmvemcd.jl | 1 + 11 files changed, 961 insertions(+), 213 deletions(-) create mode 100644 src/bchplot.jl diff --git a/CHANGELOG.md b/CHANGELOG.md index f9ab2ad..55540ee 100644 --- a/CHANGELOG.md +++ b/CHANGELOG.md @@ -1,5 +1,10 @@ -# Upcoming Release +# Upcoming Release + + +# 0.9.0 - Add exact argument for LAD. If exact is true then the linear programming based exact solution is found. Otherwise, a GA based search is performed to yield approximate solutions. +- Remove dependency of Plots.jl. If Plots.jl is installed and loaded manually, the functionality that uses Plot is autmatically loaded by Requires.jl. Affected functions are `dataimage`, `mveltsplot`, and `bchplot`. + # v0.8.19 - Update Satman(2013) algorithm diff --git a/Project.toml b/Project.toml index 612e713..f3df562 100644 --- a/Project.toml +++ b/Project.toml @@ -1,7 +1,7 @@ name = "LinRegOutliers" uuid = "6d4de0fb-32d9-4c65-aac1-cc9ed8b94b1a" authors = ["Mehmet Hakan Satman ", "Shreesh Adiga <16567adigashreesh@gmail.com>", "Guillermo Angeris ", "Emre Akadal "] -version = "0.8.19" +version = "0.9.0" [deps] Clustering = "aaaa29a8-35af-508c-8bc3-b662a17a0fe5" @@ -11,7 +11,7 @@ Distributions = "31c24e10-a181-5473-b8eb-7969acd0382f" GLPK = "60bf3e95-4087-53dc-ae20-288a0d20c6a6" JuMP = "4076af6c-e467-56ae-b986-b466b2749572" LinearAlgebra = "37e2e46d-f89d-539d-b4ee-838fcccc9c8e" -Plots = "91a5bcdd-55d7-5caf-9e0b-520d859cae80" +Requires = "ae029012-a4dd-5104-9daa-d747884805df" StatsModels = "3eaba693-59b7-5ba5-a881-562e759f1c8d" [compat] @@ -21,6 +21,6 @@ DataFrames = "0.22, 1" Distributions = "0.17, 0.18, 0.19.1, 0.20, 0.21, 0.22, 0.23, 0.24, 0.25" GLPK = "0.10, 0.11, 0.12, 0.13, 0.14, 0.15, 1" JuMP = "0.19, 0.20, 0.21, 0.22, 0.23, 1" -Plots = "0.22.2, 0.23, 0.24, 0.25, 0.26, 0.27, 0.28, 0.29, 1" +Requires = "1" StatsModels = "0.4, 0.5, 0.6" julia = "1.4" diff --git a/src/LinRegOutliers.jl b/src/LinRegOutliers.jl index 785d7f5..1419a04 100644 --- a/src/LinRegOutliers.jl +++ b/src/LinRegOutliers.jl @@ -1,5 +1,26 @@ module LinRegOutliers +using Requires + +# After the module is loaded, we check if Plots is installed and loaded. +# If Plots is installed and loaded, we load the corresponding modules. +function __init__() + @require Plots = "91a5bcdd-55d7-5caf-9e0b-520d859cae80" begin + + import .Plots: RGB + + include("mveltsplot.jl") + include("dataimage.jl") + include("bchplot.jl") + + import .MVELTSPlot: mveltsplot + import .DataImage: dataimage + import .BCHPlot: bchplot + + export mveltsplot, dataimage, bchplot, RGB + + end +end # Basis include("basis.jl") @@ -91,13 +112,14 @@ import .ASM2000: asm2000 include("mve.jl") import .MVE: mve, mcd +# Moved into grahhics.jl # MVE - LTS plot for visual detection of regression outliers -include("mveltsplot.jl") -import .MVELTSPlot: mveltsplot +#include("mveltsplot.jl") +#import .MVELTSPlot: mveltsplot # Billor & Chatterjee & Hadi Algorithm for detecting outliers include("bch.jl") -import .BCH: bch, bchplot +import .BCH: bch # Pena & Yohai (1995) algorithm include("py95.jl") @@ -138,9 +160,10 @@ include("hadi1994.jl") import .Hadi94: hadi1994 +# Moved into graphics.jl # Gray-scale images of distance matrices -include("dataimage.jl") -import .DataImage: dataimage +#include("dataimage.jl") +#import .DataImage: dataimage # Modified and original Satman (2012) algorithms @@ -218,8 +241,7 @@ export asm2000 export lms export lts export mve, mcd -export mveltsplot -export bch, bchplot +export bch export py95, py95SuspectedObservations export satman2013 export satman2015, dominates @@ -228,7 +250,6 @@ export quantileregression export lta export hadi1992 export hadi1994 -export dataimage export gwcga, galts, ga, cga, RealChromosome export detectOutliers export ransac diff --git a/src/bch.jl b/src/bch.jl index 77cbef6..5844c7f 100644 --- a/src/bch.jl +++ b/src/bch.jl @@ -1,7 +1,7 @@ module BCH export bch -export bchplot + import ..Basis: RegressionSetting, @extractRegressionSetting, designMatrix, responseVector, applyColumns @@ -13,8 +13,6 @@ import Distributions: Chisq import LinearAlgebra: diag import DataFrames: DataFrame -using Plots - """ @@ -207,59 +205,5 @@ end -""" - - bchplot(setting::RegressionSetting; alpha=0.05, maxiter=1000, epsilon=0.00001) - -Perform the Billor & Chatterjee & Hadi (2006) algorithm and generates outlier plot -for the given regression setting. - -# Arguments -- `setting::RegressionSetting`: RegressionSetting object with a formula and dataset. -- `alpha::Float64`: Optional argument of the probability of rejecting the null hypothesis. -- `maxiter::Int`: Maximum number of iterations for calculating iterative weighted least squares estimates. -- `epsilon::Float64`: Accuracy for determining convergency. - - -# References -Billor, Nedret, Samprit Chatterjee, and Ali S. Hadi. "A re-weighted least squares method -for robust regression estimation." American journal of mathematical and management sciences 26.3-4 (2006): 229-252. -""" -function bchplot( - setting::RegressionSetting; - alpha = 0.05, - maxiter = 1000, - epsilon = 0.00001, -) - X = designMatrix(setting) - y = responseVector(setting) - return bchplot(X, y, alpha = alpha, maxiter = maxiter, epsilon = epsilon) -end - -function bchplot( - Xdesign::Array{Float64,2}, - y::Array{Float64,1}; - alpha = 0.05, - maxiter = 1000, - epsilon = 0.00001, -) - result = bch(Xdesign, y, alpha = alpha, maxiter = maxiter, epsilon = epsilon) - squared_normalized_residuals = result["squared.normalized.residuals"] - squared_normalized_robust_distances = result["squared.normalized.robust.distances"] - n = length(squared_normalized_robust_distances) - scplot = scatter( - squared_normalized_robust_distances, - squared_normalized_residuals, - legend = false, - series_annotations = text.(1:n, :bottom), - tickfont = font(10), - guidefont = font(10), - labelfont = font(10), - ) - title!("Billor & Chatterjee & Hadi Plot") - xlabel!("Squared Normalized Robust Distances") - ylabel!("Squared Normalized Residuals") -end - end # end of module BCH diff --git a/src/bchplot.jl b/src/bchplot.jl new file mode 100644 index 0000000..358a1f4 --- /dev/null +++ b/src/bchplot.jl @@ -0,0 +1,69 @@ +module BCHPlot + + +export bchplot + + +import ..BCH: bch +import ..Basis: RegressionSetting + + +""" + + bchplot(setting::RegressionSetting; alpha=0.05, maxiter=1000, epsilon=0.00001) + +Perform the Billor & Chatterjee & Hadi (2006) algorithm and generates outlier plot +for the given regression setting. + +# Arguments +- `setting::RegressionSetting`: RegressionSetting object with a formula and dataset. +- `alpha::Float64`: Optional argument of the probability of rejecting the null hypothesis. +- `maxiter::Int`: Maximum number of iterations for calculating iterative weighted least squares estimates. +- `epsilon::Float64`: Accuracy for determining convergency. + + +# References +Billor, Nedret, Samprit Chatterjee, and Ali S. Hadi. "A re-weighted least squares method +for robust regression estimation." American journal of mathematical and management sciences 26.3-4 (2006): 229-252. + +!!! warning "Dependencies" + This method is enabled when the Plots package is installed and loaded. + +""" +function bchplot( + setting::RegressionSetting; + alpha = 0.05, + maxiter = 1000, + epsilon = 0.00001, +) + X = designMatrix(setting) + y = responseVector(setting) + return bchplot(X, y, alpha = alpha, maxiter = maxiter, epsilon = epsilon) +end + +function bchplot( + Xdesign::Array{Float64,2}, + y::Array{Float64,1}; + alpha = 0.05, + maxiter = 1000, + epsilon = 0.00001, +) + result = bch(Xdesign, y, alpha = alpha, maxiter = maxiter, epsilon = epsilon) + squared_normalized_residuals = result["squared.normalized.residuals"] + squared_normalized_robust_distances = result["squared.normalized.robust.distances"] + n = length(squared_normalized_robust_distances) + scplot = scatter( + squared_normalized_robust_distances, + squared_normalized_residuals, + legend = false, + series_annotations = text.(1:n, :bottom), + tickfont = font(10), + guidefont = font(10), + labelfont = font(10), + ) + title!("Billor & Chatterjee & Hadi Plot") + xlabel!("Squared Normalized Robust Distances") + ylabel!("Squared Normalized Residuals") +end + +end # end of module \ No newline at end of file diff --git a/src/dataimage.jl b/src/dataimage.jl index cf9a8a5..a3489a3 100644 --- a/src/dataimage.jl +++ b/src/dataimage.jl @@ -1,14 +1,11 @@ module DataImage - +export dataimage import ..Diagnostics: mahalanobisSquaredMatrix, euclideanDistances, mahalanobisSquaredBetweenPairs - -import Plots: RGB - - +import ..RGB """ @@ -41,11 +38,15 @@ julia> Plots.plot(di) # References Marchette, David J., and Jeffrey L. Solka. "Using data images for outlier detection." Computational Statistics & Data Analysis 43.4 (2003): 541-552. + +!!! warning "Dependencies" + This method is enabled when the Plots package is installed and loaded. + """ function dataimage( dataMatrix::Array{Float64,2}; distance = :mahalanobis, -)::Array{RGB{Float64},2} +)::Matrix{RGB{Float64}} d = nothing if distance == :mahalanobis d = mahalanobisSquaredBetweenPairs(dataMatrix) diff --git a/src/mveltsplot.jl b/src/mveltsplot.jl index 4e336f3..bb3f739 100644 --- a/src/mveltsplot.jl +++ b/src/mveltsplot.jl @@ -9,7 +9,6 @@ import ..MVE: mve import Distributions: Chisq, quantile -using Plots """ mveltsplot(setting; alpha = 0.05, showplot = true) @@ -41,6 +40,10 @@ good leverage points (observations far from the remaining of data in both x and # References Van Aelst, Stefan, and Peter Rousseeuw. "Minimum volume ellipsoid." Wiley Interdisciplinary Reviews: Computational Statistics 1.1 (2009): 71-82. + +!!! warning "Dependencies" + This method is enabled when the Plots package is installed and loaded. + """ function mveltsplot(setting::RegressionSetting; alpha = 0.05, showplot = true) ltsresult = lts(setting) diff --git a/test/Manifest.toml b/test/Manifest.toml index 8a83293..3db8370 100644 --- a/test/Manifest.toml +++ b/test/Manifest.toml @@ -1,331 +1,1035 @@ # This file is machine-generated - editing it directly is not advised -[[ArgTools]] +julia_version = "1.9.0-beta2" +manifest_format = "2.0" +project_hash = "51707d4d699ef911652821ffdc02fa35e0ffe382" + +[[deps.ArgTools]] uuid = "0dad84c5-d112-42e6-8d28-ef12dabb789f" +version = "1.1.1" -[[Artifacts]] +[[deps.Artifacts]] uuid = "56f22d72-fd6d-98f1-02f0-08ddc0907c33" -[[Base64]] +[[deps.Base64]] uuid = "2a0f44e3-6c83-55bd-87e4-b1978d98bd5f" -[[ChainRulesCore]] +[[deps.BitFlags]] +git-tree-sha1 = "43b1a4a8f797c1cddadf60499a8a077d4af2cd2d" +uuid = 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+[[deps.libvorbis_jll]] +deps = ["Artifacts", "JLLWrappers", "Libdl", "Ogg_jll", "Pkg"] +git-tree-sha1 = "b910cb81ef3fe6e78bf6acee440bda86fd6ae00c" +uuid = "f27f6e37-5d2b-51aa-960f-b287f2bc3b7a" +version = "1.3.7+1" + +[[deps.nghttp2_jll]] deps = ["Artifacts", "Libdl"] uuid = "8e850ede-7688-5339-a07c-302acd2aaf8d" +version = "1.48.0+0" -[[p7zip_jll]] +[[deps.p7zip_jll]] deps = ["Artifacts", "Libdl"] uuid = "3f19e933-33d8-53b3-aaab-bd5110c3b7a0" +version = "17.4.0+0" + +[[deps.x264_jll]] +deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg"] +git-tree-sha1 = "4fea590b89e6ec504593146bf8b988b2c00922b2" +uuid = "1270edf5-f2f9-52d2-97e9-ab00b5d0237a" +version = "2021.5.5+0" + +[[deps.x265_jll]] +deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg"] +git-tree-sha1 = "ee567a171cce03570d77ad3a43e90218e38937a9" +uuid = "dfaa095f-4041-5dcd-9319-2fabd8486b76" +version = "3.5.0+0" + +[[deps.xkbcommon_jll]] +deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg", "Wayland_jll", "Wayland_protocols_jll", "Xorg_libxcb_jll", "Xorg_xkeyboard_config_jll"] +git-tree-sha1 = "9ebfc140cc56e8c2156a15ceac2f0302e327ac0a" +uuid = "d8fb68d0-12a3-5cfd-a85a-d49703b185fd" +version = "1.4.1+0" diff --git a/test/testdataimage.jl b/test/testdataimage.jl index b440d67..16aad5b 100644 --- a/test/testdataimage.jl +++ b/test/testdataimage.jl @@ -1,4 +1,5 @@ @testset "DataImage" begin + datamat = [ 1.0 2 3 4 5 6 diff --git a/test/testga.jl b/test/testga.jl index c489310..b836816 100644 --- a/test/testga.jl +++ b/test/testga.jl @@ -6,7 +6,6 @@ return sum(bits) end result = cga(chsize = 10, costfunction = fcost, popsize = 100) - @warn result for element in result @test (element == 0) || (element == 1) end diff --git a/test/testmvemcd.jl b/test/testmvemcd.jl index 156562d..dca9f56 100644 --- a/test/testmvemcd.jl +++ b/test/testmvemcd.jl @@ -26,6 +26,7 @@ @testset "MVE & LTS Plot - Algorithm - Phone data" begin + using Plots df = phones reg = createRegressionSetting(@formula(calls ~ year), df) result = mveltsplot(reg, showplot = false)