Add tests with Aqua, ExplicitImports and JET (#189)

* Require Julia 1.10 and remove Parameters

* Add tests with Aqua, ExplicitImports and JET

Author: devmotion

.github/workflows/ci.yml

@@ -2,24 +2,24 @@ name: Run tests
 
 on: [push, pull_request]
 
+# needed to allow julia-actions/cache to delete old caches that it has created
+permissions:
+  actions: write
+  contents: read
+
 jobs:
   test:
     runs-on: ${{ matrix.os }}
     strategy:
       matrix:
-        julia-version: ['1.6', '1', 'nightly']
-        julia-arch: [x64, x86]
+        julia-version: ['min', 'lts', '1']
         os: [ubuntu-latest, windows-latest, macOS-latest]
-        exclude:
-          - os: macOS-latest
-            julia-arch: x86
-
     steps:
       - uses: actions/checkout@v2
-      - uses: julia-actions/setup-julia@v1
+      - uses: julia-actions/setup-julia@v2
         with:
           version: ${{ matrix.julia-version }}
-          arch: ${{ matrix.julia-arch }}
+      - uses: julia-actions/cache@v2
       - uses: julia-actions/julia-buildpkg@v1
       - uses: julia-actions/julia-runtest@v1
         with:

Project.toml

@@ -1,28 +1,35 @@
 name = "LineSearches"
 uuid = "d3d80556-e9d4-5f37-9878-2ab0fcc64255"
-version = "7.4.1"
+version = "7.5.0"
 
 [deps]
 LinearAlgebra = "37e2e46d-f89d-539d-b4ee-838fcccc9c8e"
 NLSolversBase = "d41bc354-129a-5804-8e4c-c37616107c6c"
 NaNMath = "77ba4419-2d1f-58cd-9bb1-8ffee604a2e3"
-Parameters = "d96e819e-fc66-5662-9728-84c9c7592b0a"
 Printf = "de0858da-6303-5e67-8744-51eddeeeb8d7"
 
 [compat]
+Aqua = "0.8.14"
 DoubleFloats = "1"
+ExplicitImports = "1.14"
+JET = "0.9, 0.10"
+LinearAlgebra = "<0.0.1, 1"
 NLSolversBase = "7"
 NaNMath = "1"
 Optim = "1"
 OptimTestProblems = "2"
-Parameters = "0.10, 0.11, 0.12"
-julia = "1.6"
+Printf = "<0.0.1, 1"
+Test = "<0.0.1, 1"
+julia = "1.10"
 
 [extras]
+Aqua = "4c88cf16-eb10-579e-8560-4a9242c79595"
 DoubleFloats = "497a8b3b-efae-58df-a0af-a86822472b78"
+ExplicitImports = "7d51a73a-1435-4ff3-83d9-f097790105c7"
+JET = "c3a54625-cd67-489e-a8e7-0a5a0ff4e31b"
 Optim = "429524aa-4258-5aef-a3af-852621145aeb"
 OptimTestProblems = "cec144fc-5a64-5bc6-99fb-dde8f63e154c"
 Test = "8dfed614-e22c-5e08-85e1-65c5234f0b40"
 
 [targets]
-test = ["Test", "OptimTestProblems", "Optim", "DoubleFloats"]
+test = ["Aqua", "ExplicitImports", "JET", "Test", "OptimTestProblems", "Optim", "DoubleFloats"]

README.md

@@ -3,6 +3,7 @@
 [![Build Status](https://travis-ci.org/JuliaNLSolvers/LineSearches.jl.svg?branch=master)](https://travis-ci.org/JuliaNLSolvers/LineSearches.jl)
 [![Codecov branch](https://img.shields.io/codecov/c/github/JuliaNLSolvers/LineSearches.jl/master.svg?maxAge=2592000)](https://codecov.io/gh/JuliaNLSolvers/LineSearches.jl)
 [![][docs-stable-img]][docs-stable-url]
+[![Aqua QA](https://raw.githubusercontent.com/JuliaTesting/Aqua.jl/master/badge.svg)](https://github.com/JuliaTesting/Aqua.jl)
 
 ## Description
 This package provides an interface to line search algorithms implemented in Julia.

src/LineSearches.jl

@@ -1,11 +1,9 @@
 module LineSearches
 
-using Printf
-import LinearAlgebra: dot, norm
-using Parameters, NaNMath
-
-import NLSolversBase
-import NLSolversBase: AbstractObjective
+using Printf: @sprintf
+using LinearAlgebra: dot, norm
+using NaNMath: NaNMath
+using NLSolversBase: NLSolversBase, AbstractObjective
 
 export LineSearchException, LineSearchCache
 

src/backtracking.jl

@@ -8,7 +8,7 @@ there exists a factor ρ = ρ(c₁) such that α' ≦ ρ α.
 
 This is a modification of the algorithm described in Nocedal Wright (2nd ed), Sec. 3.5.
 """
-@with_kw struct BackTracking{TF, TI} <: AbstractLineSearch
+@kwdef struct BackTracking{TF, TI} <: AbstractLineSearch
     c_1::TF = 1e-4
     ρ_hi::TF = 0.5
     ρ_lo::TF = 0.1
@@ -38,7 +38,7 @@ end
 
 # TODO: Should we deprecate the interface that only uses the ϕ argument?
 function (ls::BackTracking)(ϕ, αinitial::Tα, ϕ_0, dϕ_0) where Tα
-    @unpack c_1, ρ_hi, ρ_lo, iterations, order, cache = ls
+    (; c_1, ρ_hi, ρ_lo, iterations, order, cache) = ls
     emptycache!(cache)
     pushcache!(cache, 0, ϕ_0, dϕ_0)  # backtracking doesn't use the slope except here
 

src/hagerzhang.jl

@@ -80,7 +80,7 @@ Conjugate gradient line search implementation from:
     conjugate gradient method with guaranteed descent. ACM
     Transactions on Mathematical Software 32: 113–137.
 """
-@with_kw struct HagerZhang{T, Tm} <: AbstractLineSearch
+@kwdef struct HagerZhang{T, Tm} <: AbstractLineSearch
    delta::T = DEFAULTDELTA # c_1 Wolfe sufficient decrease condition
    sigma::T = DEFAULTSIGMA # c_2 Wolfe curvature condition (Recommend 0.1 for GradientDescent)
    alphamax::T = Inf
@@ -110,8 +110,8 @@ function (ls::HagerZhang)(ϕ, ϕdϕ,
                           c::T,
                           phi_0::Real,
                           dphi_0::Real) where T # Should c and phi_0 be same type?
-    @unpack delta, sigma, alphamax, rho, epsilon, gamma,
-            linesearchmax, psi3, display, mayterminate, cache = ls
+    (; delta, sigma, alphamax, rho, epsilon, gamma,
+            linesearchmax, psi3, display, mayterminate, cache) = ls
     emptycache!(cache)
 
     zeroT = convert(T, 0)
@@ -129,7 +129,7 @@ function (ls::HagerZhang)(ϕ, ϕdϕ,
     # ϕ(x_new) infinite
     iterfinitemax::Int = ceil(Int, -log2(eps(T)))
     if cache !== nothing
-        @unpack alphas, values, slopes = cache
+        (; alphas, values, slopes) = cache
     else
         alphas = [zeroT] # for bisection
         values = [phi_0]

src/initialguess.jl

@@ -7,7 +7,7 @@ Keyword `alpha` corresponds to static step length, default is 1.0.
 If keyword `scaled = true`, then the initial step length
 is scaled with the `l_2` norm of the step direction.
 """
-@with_kw struct InitialStatic{T}
+@kwdef struct InitialStatic{T}
     alpha::T = 1.0
     scaled::Bool = false # Scales step. alpha ← min(alpha,||s||_2) / ||s||_2
 end
@@ -29,7 +29,7 @@ within the bounds [alphamin, alphamax]
 
 If state.alpha is NaN, then return fallback value is.alpha
 """
-@with_kw struct InitialPrevious{T}
+@kwdef struct InitialPrevious{T}
     alpha::T = 1.0
     alphamin::T = 0.0
     alphamax::T = Inf
@@ -61,7 +61,7 @@ This procedure have several arguments, with the following defaults.
 
 If αmax ≠ 1.0, then you should consider to ensure that snap2one[2] < αmax.
 """
-@with_kw struct InitialQuadratic{T}
+@kwdef struct InitialQuadratic{T}
     αmin::T = 1e-12 # Minimum initial step size (value somewhat arbitrary)
     αmax::T = 1.0   # Maximum initial step size (advised by Nocedal+Wright)
     α0::T   = 1.0   # Fallback at first iteration
@@ -121,7 +121,7 @@ function InitialConstantChange{T}(; αmin = 1e-12,
                         snap2one = (0.75, Inf)) where T
     αmin, αmax, α0, ρ = convert.(T, (αmin, αmax, α0, ρ))
     snap2one = convert.(T, snap2one)
-    InitialConstantChange(αmin, αmax, α0, ρ, snap2one, Ref{T}(convert(T, NaN)))
+    InitialConstantChange(αmin, αmax, α0, ρ, snap2one, Ref(convert(T, NaN)))
 end
 
 # Have to make this constructor without with_kw because Ref(NaN) has to adapt to T
@@ -131,7 +131,7 @@ function InitialConstantChange(; αmin = 1e-12,
                         ρ    = 0.25,
                         snap2one = (0.75, Inf))
     T = promote_type(typeof.((αmin, αmax, α0, ρ))...)
-    InitialConstantChange(αmin, αmax, α0, ρ, snap2one, Ref{T}(convert(T, NaN)))
+    InitialConstantChange(αmin, αmax, α0, ρ, snap2one, Ref(convert(T, NaN)))
 end
 
 function (is::InitialConstantChange{T})(ls, state, phi_0, dphi_0, df) where T
@@ -163,7 +163,7 @@ Initial step size algorithm from
 If α0 is NaN, then procedure I0 is called at the first iteration,
 otherwise, we select according to procedure I1-2, with starting value α0.
 """
-@with_kw struct InitialHagerZhang{T}
+@kwdef struct InitialHagerZhang{T}
     ψ0::T          = 0.01
     ψ1::T          = 0.2
     ψ2::T          = 2.0

src/morethuente.jl

@@ -138,7 +138,7 @@ The line search implementation from:
     Line search algorithms with guaranteed sufficient decrease.
     ACM Transactions on Mathematical Software (TOMS) 20.3 (1994): 286-307.
 """
-@with_kw struct MoreThuente{T} <: AbstractLineSearch
+@kwdef struct MoreThuente{T} <: AbstractLineSearch
     f_tol::T = 1e-4 # c_1 Wolfe sufficient decrease condition
     gtol::T = 0.9   # c_2 Wolfe curvature condition (Recommend 0.1 for GradientDescent)
     x_tol::T = 1e-8
@@ -162,7 +162,7 @@ function (ls::MoreThuente)(ϕdϕ,
                            alpha::T,
                            ϕ_0,
                            dϕ_0) where T
-    @unpack f_tol, gtol, x_tol, alphamin, alphamax, maxfev, cache = ls
+    (; f_tol, gtol, x_tol, alphamin, alphamax, maxfev, cache) = ls
     emptycache!(cache)
 
     iterfinitemax = -log2(eps(T))

src/strongwolfe.jl

@@ -14,7 +14,7 @@ use `MoreThuente`, `HagerZhang` or `BackTracking`.
 * `c_2 = 0.9` : second (strong) Wolfe condition
 * `ρ = 2.0` : bracket growth
 """
-@with_kw struct StrongWolfe{T} <: AbstractLineSearch
+@kwdef struct StrongWolfe{T} <: AbstractLineSearch
     c_1::T = 1e-4
     c_2::T = 0.9
     ρ::T = 2.0
@@ -50,7 +50,7 @@ Both `alpha` and `ϕ(alpha)` are returned.
 """
 function (ls::StrongWolfe)(ϕ, dϕ, ϕdϕ,
                            alpha0::T, ϕ_0, dϕ_0) where T<:Real
-    @unpack c_1, c_2, ρ, cache = ls
+    (; c_1, c_2, ρ, cache) = ls
     emptycache!(cache)
 
     zeroT = convert(T, 0)

test/qa.jl

@@ -0,0 +1,46 @@
+using LineSearches
+using Test
+import Aqua
+import ExplicitImports
+import JET
+
+@testset "QA" begin
+    @testset "Aqua" begin
+        Aqua.test_all(LineSearches)
+    end
+
+    @testset "ExplicitImports" begin
+        # No implicit imports (`using XY`)
+        @test ExplicitImports.check_no_implicit_imports(LineSearches) === nothing
+
+        # All explicit imports (`using XY: Z`) are loaded via their owners
+        @test ExplicitImports.check_all_explicit_imports_via_owners(LineSearches) === nothing
+
+        # No explicit imports (`using XY: Z`) that are not used
+        @test ExplicitImports.check_no_stale_explicit_imports(LineSearches) === nothing
+
+        # Nothing is accessed via modules other than its owner
+        @test ExplicitImports.check_all_qualified_accesses_via_owners(LineSearches) === nothing
+
+        # Almost no accesses of non-public names
+        @test ExplicitImports.check_all_qualified_accesses_are_public(
+            LineSearches;
+            ignore = (
+                :RefValue, # Base
+                :max, # NaNMath
+                :min, # NaNMath
+            ),
+        ) === nothing
+
+        # No self-qualified accesses
+        @test ExplicitImports.check_no_self_qualified_accesses(LineSearches) === nothing
+    end
+
+    @testset "JET" begin
+        # Check that there are no undefined global references and undefined field accesses
+        JET.test_package(LineSearches; target_defined_modules = true, mode = :typo)
+
+        # Analyze methods based on their declared signature
+        JET.test_package(LineSearches; target_defined_modules = true)
+    end
+end