Fixing package dependencies to work with Julia LTS

pebeto · pebeto · commit c0ce30a28595 · 2025-11-06T12:01:56.000-05:00
diff --git a/Project.toml b/Project.toml
@@ -1,7 +1,7 @@
 name = "MLJLinearModels"
 uuid = "6ee0df7b-362f-4a72-a706-9e79364fb692"
-authors = ["Thibaut Lienart <tlienart@me.com>"]
 version = "0.10.1"
+authors = ["Thibaut Lienart <tlienart@me.com>"]
 
 [deps]
 DocStringExtensions = "ffbed154-4ef7-542d-bbb7-c09d3a79fcae"
@@ -13,10 +13,30 @@ Optim = "429524aa-4258-5aef-a3af-852621145aeb"
 Parameters = "d96e819e-fc66-5662-9728-84c9c7592b0a"
 
 [compat]
-DocStringExtensions = "0.8, 0.9"
-IterativeSolvers = "0.8, 0.9"
-LinearMaps = "2.6, 3.2"
-MLJModelInterface = "1.4"
-Optim = "0.20, 0.21, 1"
-Parameters = "0.12"
-julia = "1.6, 1"
+DocStringExtensions = "0.9.5"
+IterativeSolvers = "0.9.4"
+LinearMaps = "3.11.4"
+MLJModelInterface = "1.12.0"
+Optim = "1.13.2"
+Parameters = "0.12.3"
+julia = "1.10"
+
+[extras]
+CSV = "336ed68f-0bac-5ca0-87d4-7b16caf5d00b"
+DataFrames = "a93c6f00-e57d-5684-b7b6-d8193f3e46c0"
+DelimitedFiles = "8bb1440f-4735-579b-a4ab-409b98df4dab"
+Downloads = "f43a241f-c20a-4ad4-852c-f6b1247861c6"
+ForwardDiff = "f6369f11-7733-5829-9624-2563aa707210"
+LinearAlgebra = "37e2e46d-f89d-539d-b4ee-838fcccc9c8e"
+MLJ = "add582a8-e3ab-11e8-2d5e-e98b27df1bc7"
+MLJBase = "a7f614a8-145f-11e9-1d2a-a57a1082229d"
+Optim = "429524aa-4258-5aef-a3af-852621145aeb"
+PyCall = "438e738f-606a-5dbb-bf0a-cddfbfd45ab0"
+RCall = "6f49c342-dc21-5d91-9882-a32aef131414"
+RDatasets = "ce6b1742-4840-55fa-b093-852dadbb1d8b"
+Random = "9a3f8284-a2c9-5f02-9a11-845980a1fd5c"
+StableRNGs = "860ef19b-820b-49d6-a774-d7a799459cd3"
+Test = "8dfed614-e22c-5e08-85e1-65c5234f0b40"
+
+[targets]
+test = ["CSV", "DataFrames", "DelimitedFiles", "Downloads", "ForwardDiff", "LinearAlgebra", "MLJ", "MLJBase", "Optim", "PyCall", "RCall", "RDatasets", "Random", "StableRNGs", "Test"]
diff --git a/tatus b/tatus
@@ -0,0 +1,91 @@
+[1mdiff --git a/Project.toml b/Project.toml[m
+[1mindex 17c63cb..a7d099d 100644[m
+[1m--- a/Project.toml[m
+[1m+++ b/Project.toml[m
+[36m@@ -1,7 +1,7 @@[m
+ name = "MLJLinearModels"[m
+ uuid = "6ee0df7b-362f-4a72-a706-9e79364fb692"[m
+[31m-authors = ["Thibaut Lienart <tlienart@me.com>"][m
+ version = "0.10.1"[m
+[32m+[m[32mauthors = ["Thibaut Lienart <tlienart@me.com>"][m
+ [m
+ [deps][m
+ DocStringExtensions = "ffbed154-4ef7-542d-bbb7-c09d3a79fcae"[m
+[36m@@ -13,10 +13,30 @@[m [mOptim = "429524aa-4258-5aef-a3af-852621145aeb"[m
+ Parameters = "d96e819e-fc66-5662-9728-84c9c7592b0a"[m
+ [m
+ [compat][m
+[31m-DocStringExtensions = "0.8, 0.9"[m
+[31m-IterativeSolvers = "0.8, 0.9"[m
+[31m-LinearMaps = "2.6, 3.2"[m
+[31m-MLJModelInterface = "1.4"[m
+[31m-Optim = "0.20, 0.21, 1"[m
+[31m-Parameters = "0.12"[m
+[31m-julia = "1.6, 1"[m
+[32m+[m[32mDocStringExtensions = "0.9.5"[m
+[32m+[m[32mIterativeSolvers = "0.9.4"[m
+[32m+[m[32mLinearMaps = "3.11.4"[m
+[32m+[m[32mMLJModelInterface = "1.12.0"[m
+[32m+[m[32mOptim = "1.13.2"[m
+[32m+[m[32mParameters = "0.12.3"[m
+[32m+[m[32mjulia = "1.10"[m
+[32m+[m
+[32m+[m[32m[extras][m
+[32m+[m[32mCSV = "336ed68f-0bac-5ca0-87d4-7b16caf5d00b"[m
+[32m+[m[32mDataFrames = "a93c6f00-e57d-5684-b7b6-d8193f3e46c0"[m
+[32m+[m[32mDelimitedFiles = "8bb1440f-4735-579b-a4ab-409b98df4dab"[m
+[32m+[m[32mDownloads = "f43a241f-c20a-4ad4-852c-f6b1247861c6"[m
+[32m+[m[32mForwardDiff = "f6369f11-7733-5829-9624-2563aa707210"[m
+[32m+[m[32mLinearAlgebra = "37e2e46d-f89d-539d-b4ee-838fcccc9c8e"[m
+[32m+[m[32mMLJ = "add582a8-e3ab-11e8-2d5e-e98b27df1bc7"[m
+[32m+[m[32mMLJBase = "a7f614a8-145f-11e9-1d2a-a57a1082229d"[m
+[32m+[m[32mOptim = "429524aa-4258-5aef-a3af-852621145aeb"[m
+[32m+[m[32mPyCall = "438e738f-606a-5dbb-bf0a-cddfbfd45ab0"[m
+[32m+[m[32mRCall = "6f49c342-dc21-5d91-9882-a32aef131414"[m
+[32m+[m[32mRDatasets = "ce6b1742-4840-55fa-b093-852dadbb1d8b"[m
+[32m+[m[32mRandom = "9a3f8284-a2c9-5f02-9a11-845980a1fd5c"[m
+[32m+[m[32mStableRNGs = "860ef19b-820b-49d6-a774-d7a799459cd3"[m
+[32m+[m[32mTest = "8dfed614-e22c-5e08-85e1-65c5234f0b40"[m
+[32m+[m
+[32m+[m[32m[targets][m
+[32m+[m[32mtest = ["CSV", "DataFrames", "DelimitedFiles", "Downloads", "ForwardDiff", "LinearAlgebra", "MLJ", "MLJBase", "Optim", "PyCall", "RCall", "RDatasets", "Random", "StableRNGs", "Test"][m
+[1mdiff --git a/test/Project.toml b/test/Project.toml[m
+[1mindex ac1e430..498be2e 100644[m
+[1m--- a/test/Project.toml[m
+[1m+++ b/test/Project.toml[m
+[36m@@ -14,13 +14,3 @@[m [mRDatasets = "ce6b1742-4840-55fa-b093-852dadbb1d8b"[m
+ Random = "9a3f8284-a2c9-5f02-9a11-845980a1fd5c"[m
+ StableRNGs = "860ef19b-820b-49d6-a774-d7a799459cd3"[m
+ Test = "8dfed614-e22c-5e08-85e1-65c5234f0b40"[m
+[31m-[m
+[31m-[compat][m
+[31m-DataFrames = "1.4"[m
+[31m-ForwardDiff = "0.10"[m
+[31m-MLJ = "0.19"[m
+[31m-MLJBase = "0.21"[m
+[31m-PyCall = "1.95"[m
+[31m-RCall = "0.13"[m
+[31m-RDatasets = "0.7"[m
+[31m-StableRNGs = "1.0"[m
+[1mdiff --git a/test/interface/fitpredict.jl b/test/interface/fitpredict.jl[m
+[1mindex 3e15c55..8687d8f 100644[m
+[1m--- a/test/interface/fitpredict.jl[m
+[1m+++ b/test/interface/fitpredict.jl[m
+[36m@@ -37,7 +37,7 @@[m [mend[m
+     ŷ = MLJBase.predict(lr, fr, Xt)[m
+     ŷ = MLJBase.mode.(ŷ)[m
+ [m
+[31m-    mcr = MLJBase.misclassification_rate(ŷ, yc)[m
+[32m+[m[32m    mcr = MLJ.misclassification_rate(ŷ, yc)[m
+     @test mcr ≤ 0.2[m
+ end[m
+ [m
+[36m@@ -62,7 +62,7 @@[m [mend[m
+     ŷ = MLJBase.predict(mc, fr, Xt)[m
+     ŷ = MLJBase.mode.(ŷ)[m
+ [m
+[31m-    mcr = MLJBase.misclassification_rate(ŷ, yc)[m
+[32m+[m[32m    mcr = MLJ.misclassification_rate(ŷ, yc)[m
+     @test mcr ≤ 0.3[m
+ end[m
+ [m
diff --git a/test/Project.toml b/test/Project.toml
@@ -14,13 +14,3 @@ RDatasets = "ce6b1742-4840-55fa-b093-852dadbb1d8b"
 Random = "9a3f8284-a2c9-5f02-9a11-845980a1fd5c"
 StableRNGs = "860ef19b-820b-49d6-a774-d7a799459cd3"
 Test = "8dfed614-e22c-5e08-85e1-65c5234f0b40"
-
-[compat]
-DataFrames = "1.4"
-ForwardDiff = "0.10"
-MLJ = "0.19"
-MLJBase = "0.21"
-PyCall = "1.95"
-RCall = "0.13"
-RDatasets = "0.7"
-StableRNGs = "1.0"
diff --git a/test/interface/fitpredict.jl b/test/interface/fitpredict.jl
@@ -37,7 +37,7 @@ end
     ŷ = MLJBase.predict(lr, fr, Xt)
     ŷ = MLJBase.mode.(ŷ)
 
-    mcr = MLJBase.misclassification_rate(ŷ, yc)
+    mcr = MLJ.misclassification_rate(ŷ, yc)
     @test mcr ≤ 0.2
 end
 
@@ -62,7 +62,7 @@ end
     ŷ = MLJBase.predict(mc, fr, Xt)
     ŷ = MLJBase.mode.(ŷ)
 
-    mcr = MLJBase.misclassification_rate(ŷ, yc)
+    mcr = MLJ.misclassification_rate(ŷ, yc)
     @test mcr ≤ 0.3
 end
 

-Original file line number
+Diff line change
@@ @@ -0,0 +1,91 @@ @@
 +[1mdiff --git a/Project.toml b/Project.toml[m
 +[1mindex 17c63cb..a7d099d 100644[m
 +[1m--- a/Project.toml[m
 +[1m+++ b/Project.toml[m
 +[36m@@ -1,7 +1,7 @@[m
 + name = "MLJLinearModels"[m
 + uuid = "6ee0df7b-362f-4a72-a706-9e79364fb692"[m
 +[31m-authors = ["Thibaut Lienart <tlienart@me.com>"][m
 + version = "0.10.1"[m
 +[32m+[m[32mauthors = ["Thibaut Lienart <tlienart@me.com>"][m
 + [m
 + [deps][m
 + DocStringExtensions = "ffbed154-4ef7-542d-bbb7-c09d3a79fcae"[m
 +[36m@@ -13,10 +13,30 @@[m [mOptim = "429524aa-4258-5aef-a3af-852621145aeb"[m
 + Parameters = "d96e819e-fc66-5662-9728-84c9c7592b0a"[m
 + [m
 + [compat][m
 +[31m-DocStringExtensions = "0.8, 0.9"[m
 +[31m-IterativeSolvers = "0.8, 0.9"[m
 +[31m-LinearMaps = "2.6, 3.2"[m
 +[31m-MLJModelInterface = "1.4"[m
 +[31m-Optim = "0.20, 0.21, 1"[m
 +[31m-Parameters = "0.12"[m
 +[31m-julia = "1.6, 1"[m
 +[32m+[m[32mDocStringExtensions = "0.9.5"[m
 +[32m+[m[32mIterativeSolvers = "0.9.4"[m
 +[32m+[m[32mLinearMaps = "3.11.4"[m
 +[32m+[m[32mMLJModelInterface = "1.12.0"[m
 +[32m+[m[32mOptim = "1.13.2"[m
 +[32m+[m[32mParameters = "0.12.3"[m
 +[32m+[m[32mjulia = "1.10"[m
 +[32m+[m
 +[32m+[m[32m[extras][m
 +[32m+[m[32mCSV = "336ed68f-0bac-5ca0-87d4-7b16caf5d00b"[m
 +[32m+[m[32mDataFrames = "a93c6f00-e57d-5684-b7b6-d8193f3e46c0"[m
 +[32m+[m[32mDelimitedFiles = "8bb1440f-4735-579b-a4ab-409b98df4dab"[m
 +[32m+[m[32mDownloads = "f43a241f-c20a-4ad4-852c-f6b1247861c6"[m
 +[32m+[m[32mForwardDiff = "f6369f11-7733-5829-9624-2563aa707210"[m
 +[32m+[m[32mLinearAlgebra = "37e2e46d-f89d-539d-b4ee-838fcccc9c8e"[m
 +[32m+[m[32mMLJ = "add582a8-e3ab-11e8-2d5e-e98b27df1bc7"[m
 +[32m+[m[32mMLJBase = "a7f614a8-145f-11e9-1d2a-a57a1082229d"[m
 +[32m+[m[32mOptim = "429524aa-4258-5aef-a3af-852621145aeb"[m
 +[32m+[m[32mPyCall = "438e738f-606a-5dbb-bf0a-cddfbfd45ab0"[m
 +[32m+[m[32mRCall = "6f49c342-dc21-5d91-9882-a32aef131414"[m
 +[32m+[m[32mRDatasets = "ce6b1742-4840-55fa-b093-852dadbb1d8b"[m
 +[32m+[m[32mRandom = "9a3f8284-a2c9-5f02-9a11-845980a1fd5c"[m
 +[32m+[m[32mStableRNGs = "860ef19b-820b-49d6-a774-d7a799459cd3"[m
 +[32m+[m[32mTest = "8dfed614-e22c-5e08-85e1-65c5234f0b40"[m
 +[32m+[m
 +[32m+[m[32m[targets][m
 +[32m+[m[32mtest = ["CSV", "DataFrames", "DelimitedFiles", "Downloads", "ForwardDiff", "LinearAlgebra", "MLJ", "MLJBase", "Optim", "PyCall", "RCall", "RDatasets", "Random", "StableRNGs", "Test"][m
 +[1mdiff --git a/test/Project.toml b/test/Project.toml[m
 +[1mindex ac1e430..498be2e 100644[m
 +[1m--- a/test/Project.toml[m
 +[1m+++ b/test/Project.toml[m
 +[36m@@ -14,13 +14,3 @@[m [mRDatasets = "ce6b1742-4840-55fa-b093-852dadbb1d8b"[m
 + Random = "9a3f8284-a2c9-5f02-9a11-845980a1fd5c"[m
 + StableRNGs = "860ef19b-820b-49d6-a774-d7a799459cd3"[m
 + Test = "8dfed614-e22c-5e08-85e1-65c5234f0b40"[m
 +[31m-[m
 +[31m-[compat][m
 +[31m-DataFrames = "1.4"[m
 +[31m-ForwardDiff = "0.10"[m
 +[31m-MLJ = "0.19"[m
 +[31m-MLJBase = "0.21"[m
 +[31m-PyCall = "1.95"[m
 +[31m-RCall = "0.13"[m
 +[31m-RDatasets = "0.7"[m
 +[31m-StableRNGs = "1.0"[m
 +[1mdiff --git a/test/interface/fitpredict.jl b/test/interface/fitpredict.jl[m
 +[1mindex 3e15c55..8687d8f 100644[m
 +[1m--- a/test/interface/fitpredict.jl[m
 +[1m+++ b/test/interface/fitpredict.jl[m
 +[36m@@ -37,7 +37,7 @@[m [mend[m
 +     ŷ = MLJBase.predict(lr, fr, Xt)[m
 +     ŷ = MLJBase.mode.(ŷ)[m
 + [m
 +[31m-    mcr = MLJBase.misclassification_rate(ŷ, yc)[m
 +[32m+[m[32m    mcr = MLJ.misclassification_rate(ŷ, yc)[m
 +     @test mcr ≤ 0.2[m
 + end[m
 + [m
 +[36m@@ -62,7 +62,7 @@[m [mend[m
 +     ŷ = MLJBase.predict(mc, fr, Xt)[m
 +     ŷ = MLJBase.mode.(ŷ)[m
 + [m
 +[31m-    mcr = MLJBase.misclassification_rate(ŷ, yc)[m
 +[32m+[m[32m    mcr = MLJ.misclassification_rate(ŷ, yc)[m
 +     @test mcr ≤ 0.3[m
 + end[m
 + [m