From bf1b9b87e6a0b98fcb24c451af0c774d9e2c5da4 Mon Sep 17 00:00:00 2001 From: "Anthony D. Blaom" Date: Mon, 8 Apr 2024 09:22:26 +1200 Subject: [PATCH 1/5] add MLJTestInterface to [targets]; mv Test to [targets] --- Project.toml | 10 +++++++++- 1 file changed, 9 insertions(+), 1 deletion(-) diff --git a/Project.toml b/Project.toml index fa8fefa..80de575 100644 --- a/Project.toml +++ b/Project.toml @@ -14,7 +14,6 @@ MLJModelInterface = "e80e1ace-859a-464e-9ed9-23947d8ae3ea" NonNegLeastSquares = "b7351bd1-99d9-5c5d-8786-f205a815c4d7" Random = "9a3f8284-a2c9-5f02-9a11-845980a1fd5c" Tables = "bd369af6-aec1-5ad0-b16a-f7cc5008161c" -Test = "8dfed614-e22c-5e08-85e1-65c5234f0b40" [compat] CSV = "0.10" @@ -26,3 +25,12 @@ MLJModelInterface = "1" NonNegLeastSquares = "0.4" Tables = "1.0" julia = "1.10" + +[extras] +MLJTestInterface = "72560011-54dd-4dc2-94f3-c5de45b75ecd" +Test = "8dfed614-e22c-5e08-85e1-65c5234f0b40" + +[targets] +test = ["MLJTestInterface", "Test"] + + From 9ec2a7fb456c48859edd570d7d359de061e22551 Mon Sep 17 00:00:00 2001 From: "Anthony D. Blaom" Date: Mon, 8 Apr 2024 09:42:17 +1200 Subject: [PATCH 2/5] rm Manifest.toml --- Manifest.toml | 1127 ------------------------------------------------- 1 file changed, 1127 deletions(-) delete mode 100644 Manifest.toml diff --git a/Manifest.toml b/Manifest.toml deleted file mode 100644 index 918fff7..0000000 --- a/Manifest.toml +++ /dev/null @@ -1,1127 +0,0 @@ -# This file is machine-generated - editing it directly is not advised - -julia_version = "1.10.2" -manifest_format = "2.0" -project_hash = "c8b4c39da002626c6b5247683f7daf3694615e60" - -[[deps.ARFFFiles]] -deps = ["CategoricalArrays", "Dates", "Parsers", "Tables"] -git-tree-sha1 = "e8c8e0a2be6eb4f56b1672e46004463033daa409" -uuid = "da404889-ca92-49ff-9e8b-0aa6b4d38dc8" -version = "1.4.1" - -[[deps.Adapt]] -deps = ["LinearAlgebra", "Requires"] -git-tree-sha1 = "6a55b747d1812e699320963ffde36f1ebdda4099" -uuid = "79e6a3ab-5dfb-504d-930d-738a2a938a0e" -version = "4.0.4" -weakdeps = ["StaticArrays"] - - [deps.Adapt.extensions] - 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-[[deps.WeakRefStrings]] -deps = ["DataAPI", "InlineStrings", "Parsers"] -git-tree-sha1 = "b1be2855ed9ed8eac54e5caff2afcdb442d52c23" -uuid = "ea10d353-3f73-51f8-a26c-33c1cb351aa5" -version = "1.4.2" - -[[deps.WorkerUtilities]] -git-tree-sha1 = "cd1659ba0d57b71a464a29e64dbc67cfe83d54e7" -uuid = "76eceee3-57b5-4d4a-8e66-0e911cebbf60" -version = "1.6.1" - -[[deps.Zlib_jll]] -deps = ["Libdl"] -uuid = "83775a58-1f1d-513f-b197-d71354ab007a" -version = "1.2.13+1" - -[[deps.libblastrampoline_jll]] -deps = ["Artifacts", "Libdl"] -uuid = "8e850b90-86db-534c-a0d3-1478176c7d93" -version = "5.8.0+1" - -[[deps.nghttp2_jll]] -deps = ["Artifacts", "Libdl"] -uuid = "8e850ede-7688-5339-a07c-302acd2aaf8d" -version = "1.52.0+1" - -[[deps.p7zip_jll]] -deps = ["Artifacts", "Libdl"] -uuid = "3f19e933-33d8-53b3-aaab-bd5110c3b7a0" -version = "17.4.0+2" From 238d5e6063375e7484f696b11903f4e5b0fc53c3 Mon Sep 17 00:00:00 2001 From: "Anthony D. Blaom" Date: Mon, 8 Apr 2024 09:42:34 +1200 Subject: [PATCH 3/5] add generic MLJ interface tests --- test/runtests.jl | 13 +++++++++++++ 1 file changed, 13 insertions(+) diff --git a/test/runtests.jl b/test/runtests.jl index 3a270db..2f6a02f 100644 --- a/test/runtests.jl +++ b/test/runtests.jl @@ -1,6 +1,7 @@ using PartitionedLS using Test using MLJBase +import MLJTestInterface @testset "Testing PartitionedLS" verbose=true begin @@ -118,4 +119,16 @@ using MLJBase end end end + + @testset "generic MLJ interface tests" begin + failures, summary = MLJTestInterface.test( + [PartLS,], + MLJTestInterface.make_regression()...; + mod=@__MODULE__, + verbosity=2, # bump to debug + throw=true, # set to true to debug + ) + @test isempty(failures) + end + end From d1770732259b53aab782146faccc1bd56150d042 Mon Sep 17 00:00:00 2001 From: Roberto Esposito Date: Mon, 8 Apr 2024 08:13:34 +0200 Subject: [PATCH 4/5] Start fixing problems signaled in issues #2, #4, #5 --- .gitignore | 1 + Project.toml | 11 ++++------- src/PartitionedLS.jl | 16 +++++++++++----- src/PartitionedLSAlt.jl | 2 +- src/PartitionedLSBnB.jl | 6 +++--- src/PartitionedLSOpt.jl | 2 +- 6 files changed, 21 insertions(+), 17 deletions(-) create mode 100644 .gitignore diff --git a/.gitignore b/.gitignore new file mode 100644 index 0000000..ba39cc5 --- /dev/null +++ b/.gitignore @@ -0,0 +1 @@ +Manifest.toml diff --git a/Project.toml b/Project.toml index 80de575..4770bf7 100644 --- a/Project.toml +++ b/Project.toml @@ -5,11 +5,8 @@ version = "1.0.1" [deps] CSV = "336ed68f-0bac-5ca0-87d4-7b16caf5d00b" -DataFrames = "a93c6f00-e57d-5684-b7b6-d8193f3e46c0" DocStringExtensions = "ffbed154-4ef7-542d-bbb7-c09d3a79fcae" LinearAlgebra = "37e2e46d-f89d-539d-b4ee-838fcccc9c8e" -MLJ = "add582a8-e3ab-11e8-2d5e-e98b27df1bc7" -MLJBase = "a7f614a8-145f-11e9-1d2a-a57a1082229d" MLJModelInterface = "e80e1ace-859a-464e-9ed9-23947d8ae3ea" NonNegLeastSquares = "b7351bd1-99d9-5c5d-8786-f205a815c4d7" Random = "9a3f8284-a2c9-5f02-9a11-845980a1fd5c" @@ -17,10 +14,7 @@ Tables = "bd369af6-aec1-5ad0-b16a-f7cc5008161c" [compat] CSV = "0.10" -DataFrames = "1.6" DocStringExtensions = "0.9" -MLJ = "0.20" -MLJBase = "1" MLJModelInterface = "1" NonNegLeastSquares = "0.4" Tables = "1.0" @@ -29,8 +23,11 @@ julia = "1.10" [extras] MLJTestInterface = "72560011-54dd-4dc2-94f3-c5de45b75ecd" Test = "8dfed614-e22c-5e08-85e1-65c5234f0b40" +MLJ = "add582a8-e3ab-11e8-2d5e-e98b27df1bc7" +MLJBase = "a7f614a8-145f-11e9-1d2a-a57a1082229d" [targets] -test = ["MLJTestInterface", "Test"] +test = ["MLJTestInterface", "MLJBase", "MLJ", "Test"] + diff --git a/src/PartitionedLS.jl b/src/PartitionedLS.jl index 4fb57a7..5891672 100644 --- a/src/PartitionedLS.jl +++ b/src/PartitionedLS.jl @@ -7,7 +7,6 @@ using LinearAlgebra using NonNegLeastSquares using DocStringExtensions using MLJModelInterface -using MLJBase using Tables using Random @@ -177,7 +176,7 @@ model = PartLS(P=P, Optimizer=Alt, rng=123) """ MLJModelInterface.@mlj_model mutable struct PartLS <: MLJModelInterface.Deterministic Optimizer::Union{Type{Opt},Type{Alt},Type{BnB}} = Opt - P::Matrix{Int} = 0.5::(all(_[i, j] == 0 || _[i, j] == 1 for i in range(1, size(_, 1)) for j in range(1, size(_, 2)))) + P::Matrix{Int} = Array{Int}(undef, 0,0)::(all(_[i, j] == 0 || _[i, j] == 1 for i in range(1, size(_, 1)) for j in range(1, size(_, 2)))) η::Float64 = 0.0::(_ >= 0) ϵ::Float64 = 1e-6::(_ > 0) T::Int = 100::(_ > 0) @@ -199,13 +198,20 @@ It conforms to the MLJ interface. """ function MLJModelInterface.fit(m::PartLS, verbosity, X, y) - X = MLJBase.matrix(X) + X = MLJModelInterface.matrix(X) + y = vec(y) + P = m.P + + if size(P, 1) == 0 + @warn "P is empty, using a single partition: this corresponds to resort to a standard least squares problem and is likely not what you want." + P = ones(Int, size(X, 2), 1) + end if m.Optimizer == Alt - return PartitionedLS.fit(Alt, X, y, m.P, η=m.η, ϵ=m.ϵ, T=m.T, rng=m.rng) + return PartitionedLS.fit(Alt, X, y, P, η=m.η, ϵ=m.ϵ, T=m.T, rng=m.rng) end - return PartitionedLS.fit(m.Optimizer, X, y, m.P, η=m.η) + return PartitionedLS.fit(m.Optimizer, X, y, P, η=m.η) end function MLJModelInterface.fitted_params(model::PartLS, fitresult) diff --git a/src/PartitionedLSAlt.jl b/src/PartitionedLSAlt.jl index f5ea442..29a6eed 100644 --- a/src/PartitionedLSAlt.jl +++ b/src/PartitionedLSAlt.jl @@ -45,7 +45,7 @@ A Tuple with the following fields: 2. a `nothing` object 3. a NamedTuple with a field `opt` containing the optimal value of the objective function """ -function fit(::Type{Alt}, X::Array{Float64,2}, y::Array{Float64,1}, P::Array{Int,2}; +function fit(::Type{Alt}, X::Array{Float64,2}, y::AbstractArray{Float64,1}, P::Array{Int,2}; η = 0.0, ϵ = 1e-6, T = 100, nnlsalg = :nnls, rng = nothing) Xo, Po = homogeneousCoords(X, P) diff --git a/src/PartitionedLSBnB.jl b/src/PartitionedLSBnB.jl index 2836464..4ba440b 100644 --- a/src/PartitionedLSBnB.jl +++ b/src/PartitionedLSBnB.jl @@ -26,7 +26,7 @@ A tuple with the following fields: - `nopen` containing the number of open nodes in the branch and bound tree """ -function fit(::Type{BnB}, X::Array{Float64,2}, y::Array{Float64,1}, P::Array{Int,2}; η = 0.0, nnlsalg = :nnls) +function fit(::Type{BnB}, X::Array{Float64,2}, y::AbstractArray{Float64,1}, P::Array{Int,2}; η=0.0, nnlsalg=:nnls) Xo, Po = homogeneousCoords(X, P) Xo, yo = regularizeProblem(Xo, y, Po, η) Σ::Array{Int,1} = [] @@ -65,7 +65,7 @@ end # CSV.write("nnls_problem.csv", df) # end -function lower_bound(X::Array{Float64,2}, y::Array{Float64,1}, P::Array{Int,2}, Σ::Array{Int,1}, nnlsalg) +function lower_bound(X::Array{Float64,2}, y::AbstractArray{Float64,1}, P::Array{Int,2}, Σ::Array{Int,1}, nnlsalg) posConstr = Σ[findall(>(0), Σ)] negConstr = -Σ[findall(<(0), Σ)] @@ -90,7 +90,7 @@ function lower_bound(X::Array{Float64,2}, y::Array{Float64,1}, P::Array{Int,2}, return norm(XX * αα - y), α end -function fit_BnB(X::Array{Float64,2}, y::Array{Float64,1}, P::Matrix{Int}, μ::Float64, Σ::Array{Int,1}; +function fit_BnB(X::Array{Float64,2}, y::AbstractArray{Float64,1}, P::Matrix{Int}, μ::Float64, Σ::Array{Int,1}; depth = 0, nnlsalg = :pivot)::Tuple{Float64,Vector{Float64},Int} @debug "BnB new node" diff --git a/src/PartitionedLSOpt.jl b/src/PartitionedLSOpt.jl index 8beb0a8..4d4c3f1 100644 --- a/src/PartitionedLSOpt.jl +++ b/src/PartitionedLSOpt.jl @@ -69,7 +69,7 @@ result = fit(Opt, X, y, P) ``` """ -function fit(::Type{Opt}, X::Array{Float64,2}, y::Array{Float64,1}, P::Array{Int,2}; +function fit(::Type{Opt}, X::Array{Float64,2}, y::AbstractArray{Float64,1}, P::Array{Int,2}; η=0.0, nnlsalg=:nnls, returnAllSolutions=false) @debug "Opt algorithm fitting using non negative least square algorithm" From 8a28e0df5a8ec5715b2816abc488cc392db1fd06 Mon Sep 17 00:00:00 2001 From: Roberto Esposito Date: Mon, 8 Apr 2024 08:42:27 +0200 Subject: [PATCH 5/5] Removes dependency from MLJBase --- src/PartitionedLS.jl | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/src/PartitionedLS.jl b/src/PartitionedLS.jl index 5891672..7429e6a 100644 --- a/src/PartitionedLS.jl +++ b/src/PartitionedLS.jl @@ -226,7 +226,7 @@ It conforms to the MLJ interface. """ function MLJModelInterface.predict(model::PartLS, fitresult, X) - X = MLJBase.matrix(X) + X = MLJModelInterface.matrix(X) return PartitionedLS.predict(fitresult, X) end