Format and sparsearrays only 1.10

SciML · Jan 5, 2024 · 623214b · 623214b
1 parent 880fc54
commit 623214b
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Showing 14 changed files with 79 additions and 36 deletions.
diff --git a/Project.toml b/Project.toml
@@ -58,7 +58,7 @@ ProgressLogging = "0.1"
 Reexport = "1.2"
 ReverseDiff = "1.14"
 SciMLBase = "2.16.3"
-SparseArrays = "1.9, 1.10"
+SparseArrays = "1.10"
 SparseDiffTools = "2.14"
 SymbolicIndexingInterface = "0.3"
 Symbolics = "5.12"

diff --git a/lib/OptimizationBBO/src/OptimizationBBO.jl b/lib/OptimizationBBO/src/OptimizationBBO.jl
@@ -117,7 +117,10 @@ function SciMLBase.__solve(cache::Optimization.OptimizationCache{
         else
             n_steps = BlackBoxOptim.num_steps(trace)
             curr_u = decompose_trace(trace, cache.progress)
-            opt_state = Optimization.OptimizationState(iteration = n_steps, u = curr_u, objective = x[1], solver_state = trace)
+            opt_state = Optimization.OptimizationState(iteration = n_steps,
+                u = curr_u,
+                objective = x[1],
+                solver_state = trace)
             cb_call = cache.callback(opt_state, x...)
         end
 
@@ -178,7 +181,10 @@ function SciMLBase.__solve(cache::Optimization.OptimizationCache{
     t1 = time()
 
     opt_ret = Symbol(opt_res.stop_reason)
-    stats = Optimization.OptimizationStats(; iterations = opt_res.iterations, time = t1 - t0, fevals = opt_res.f_calls)
+    stats = Optimization.OptimizationStats(;
+        iterations = opt_res.iterations,
+        time = t1 - t0,
+        fevals = opt_res.f_calls)
     SciMLBase.build_solution(cache, cache.opt,
         BlackBoxOptim.best_candidate(opt_res),
         BlackBoxOptim.best_fitness(opt_res);

diff --git a/lib/OptimizationCMAEvolutionStrategy/src/OptimizationCMAEvolutionStrategy.jl b/lib/OptimizationCMAEvolutionStrategy/src/OptimizationCMAEvolutionStrategy.jl
@@ -22,7 +22,10 @@ function __map_optimizer_args(prob::OptimizationCache, opt::CMAEvolutionStrategy
     end
 
     mapped_args = (; lower = prob.lb,
-        upper = prob.ub, logger = CMAEvolutionStrategy.BasicLogger(prob.u0; verbosity = 0, callback = callback))
+        upper = prob.ub,
+        logger = CMAEvolutionStrategy.BasicLogger(prob.u0;
+            verbosity = 0,
+            callback = callback))
 
     if !isnothing(maxiters)
         mapped_args = (; mapped_args..., maxiter = maxiters)
@@ -105,7 +108,10 @@ function SciMLBase.__solve(cache::OptimizationCache{
     t1 = time()
 
     opt_ret = opt_res.stop.reason
-    stats = Optimization.OptimizationStats(; iterations = length(opt_res.logger.fmedian), time = t1 - t0, fevals = length(opt_res.logger.fmedian))
+    stats = Optimization.OptimizationStats(;
+        iterations = length(opt_res.logger.fmedian),
+        time = t1 - t0,
+        fevals = length(opt_res.logger.fmedian))
     SciMLBase.build_solution(cache, cache.opt,
         opt_res.logger.xbest[end],
         opt_res.logger.fbest[end]; original = opt_res,

diff --git a/lib/OptimizationCMAEvolutionStrategy/test/runtests.jl b/lib/OptimizationCMAEvolutionStrategy/test/runtests.jl
@@ -12,7 +12,7 @@ using Test
     @test 10 * sol.objective < l1
 
     function cb(state, args...)
-        if state.iteration %10 == 0
+        if state.iteration % 10 == 0
             println(state.u)
         end
         return false

diff --git a/lib/OptimizationEvolutionary/src/OptimizationEvolutionary.jl b/lib/OptimizationEvolutionary/src/OptimizationEvolutionary.jl
@@ -85,7 +85,8 @@ function SciMLBase.__solve(cache::OptimizationCache{
 
     function _cb(trace)
         curr_u = decompose_trace(trace).metadata["x"][end]
-        opt_state = Optimization.OptimizationState(; iteration = decompose_trace(trace).iteration,
+        opt_state = Optimization.OptimizationState(;
+            iteration = decompose_trace(trace).iteration,
             u = curr_u,
             objective = x[1],
             solver_state = trace)
@@ -132,8 +133,8 @@ function SciMLBase.__solve(cache::OptimizationCache{
     end
     t1 = time()
     opt_ret = Symbol(Evolutionary.converged(opt_res))
-    stats = Optimization.OptimizationStats(; iterations = opt_res.iterations
-        , time = t1 - t0, fevals = opt_res.f_calls)
+    stats = Optimization.OptimizationStats(; iterations = opt_res.iterations,
+        time = t1 - t0, fevals = opt_res.f_calls)
     SciMLBase.build_solution(cache, cache.opt,
         Evolutionary.minimizer(opt_res),
         Evolutionary.minimum(opt_res); original = opt_res,

diff --git a/lib/OptimizationEvolutionary/test/runtests.jl b/lib/OptimizationEvolutionary/test/runtests.jl
@@ -36,7 +36,7 @@ Random.seed!(1234)
     @test sol.objective < l1
 
     function cb(state, args...)
-        if state.iteration %10 == 0
+        if state.iteration % 10 == 0
             println(state.u)
         end
         return false

diff --git a/lib/OptimizationFlux/src/OptimizationFlux.jl b/lib/OptimizationFlux/src/OptimizationFlux.jl
@@ -102,7 +102,7 @@ function SciMLBase.__solve(cache::OptimizationCache{
     end
 
     t1 = time()
-    stats = Optimization.OptimizationStats(; iterations = maxiters, 
+    stats = Optimization.OptimizationStats(; iterations = maxiters,
         time = t1 - t0, fevals = maxiters, gevals = maxiters)
     SciMLBase.build_solution(cache, opt, θ, x[1], stats = stats)
     # here should be build_solution to create the output message

diff --git a/lib/OptimizationGCMAES/src/OptimizationGCMAES.jl b/lib/OptimizationGCMAES/src/OptimizationGCMAES.jl
@@ -114,12 +114,12 @@ function SciMLBase.__solve(cache::OptimizationCache{
             cache.ub; opt_args...)
     end
     t1 = time()
-    stats = Optimization.OptimizationStats(; iterations = maxiters === nothing ? 0 : maxiters, 
-            time = t1 - t0)
+    stats = Optimization.OptimizationStats(;
+        iterations = maxiters === nothing ? 0 : maxiters,
+        time = t1 - t0)
     SciMLBase.build_solution(cache, cache.opt,
         opt_xmin, opt_fmin; retcode = Symbol(Bool(opt_ret)),
-        stats = stats
-        )
+        stats = stats)
 end
 
 end
diff --git a/lib/OptimizationMOI/src/nlp.jl b/lib/OptimizationMOI/src/nlp.jl
@@ -218,7 +218,9 @@ function MOI.eval_objective(evaluator::MOIOptimizationNLPEvaluator, x)
     else
         l = evaluator.f(x, evaluator.p)
         evaluator.iteration += 1
-        state = Optimization.OptimizationState(iteration = evaluator.iteration, u = x, objective = l[1])
+        state = Optimization.OptimizationState(iteration = evaluator.iteration,
+            u = x,
+            objective = l[1])
         evaluator.callback(state, l)
         return l
     end

diff --git a/lib/OptimizationOptimJL/src/OptimizationOptimJL.jl b/lib/OptimizationOptimJL/src/OptimizationOptimJL.jl
@@ -133,8 +133,12 @@ function SciMLBase.__solve(cache::OptimizationCache{
         error("Use OptimizationFunction to pass the derivatives or automatically generate them with one of the autodiff backends")
 
     function _cb(trace)
-        θ = cache.opt isa Optim.NelderMead ? decompose_trace(trace).metadata["centroid"] : decompose_trace(trace).metadata["x"]
-        opt_state = Optimization.OptimizationState(iteration = trace.iteration, u = θ, objective = x[1], solver_state = trace)
+        θ = cache.opt isa Optim.NelderMead ? decompose_trace(trace).metadata["centroid"] :
+            decompose_trace(trace).metadata["x"]
+        opt_state = Optimization.OptimizationState(iteration = trace.iteration,
+            u = θ,
+            objective = x[1],
+            solver_state = trace)
         cb_call = cache.callback(opt_state, x...)
         if !(cb_call isa Bool)
             error("The callback should return a boolean `halt` for whether to stop the optimization process.")
@@ -208,8 +212,9 @@ function SciMLBase.__solve(cache::OptimizationCache{
     opt_res = Optim.optimize(optim_f, cache.u0, cache.opt, opt_args)
     t1 = time()
     opt_ret = Symbol(Optim.converged(opt_res))
-    stats = Optimization.OptimizationStats(; iterations = opt_res.iterations, 
-        time = t1 - t0, fevals = opt_res.f_calls, gevals = opt_res.g_calls, hevals = opt_res.h_calls)
+    stats = Optimization.OptimizationStats(; iterations = opt_res.iterations,
+        time = t1 - t0, fevals = opt_res.f_calls, gevals = opt_res.g_calls,
+        hevals = opt_res.h_calls)
     SciMLBase.build_solution(cache, cache.opt,
         opt_res.minimizer,
         cache.sense === Optimization.MaxSense ? -opt_res.minimum :
@@ -247,8 +252,13 @@ function SciMLBase.__solve(cache::OptimizationCache{
     cur, state = iterate(cache.data)
 
     function _cb(trace)
-        θ = !(cache.opt isa Optim.SAMIN) && cache.opt.method == Optim.NelderMead() ? decompose_trace(trace).metadata["centroid"] : decompose_trace(trace).metadata["x"]
-        opt_state = Optimization.OptimizationState(iteration = trace.iteration, u = θ, objective = x[1], solver_state = trace)
+        θ = !(cache.opt isa Optim.SAMIN) && cache.opt.method == Optim.NelderMead() ?
+            decompose_trace(trace).metadata["centroid"] :
+            decompose_trace(trace).metadata["x"]
+        opt_state = Optimization.OptimizationState(iteration = trace.iteration,
+            u = θ,
+            objective = x[1],
+            solver_state = trace)
         cb_call = cache.callback(opt_state, x...)
         if !(cb_call isa Bool)
             error("The callback should return a boolean `halt` for whether to stop the optimization process.")
@@ -297,8 +307,9 @@ function SciMLBase.__solve(cache::OptimizationCache{
     opt_res = Optim.optimize(optim_f, cache.lb, cache.ub, cache.u0, cache.opt, opt_args)
     t1 = time()
     opt_ret = Symbol(Optim.converged(opt_res))
-    stats = Optimization.OptimizationStats(; iterations = opt_res.iterations, 
-        time = t1 - t0, fevals = opt_res.f_calls, gevals = opt_res.g_calls, hevals = opt_res.h_calls)
+    stats = Optimization.OptimizationStats(; iterations = opt_res.iterations,
+        time = t1 - t0, fevals = opt_res.f_calls, gevals = opt_res.g_calls,
+        hevals = opt_res.h_calls)
     SciMLBase.build_solution(cache, cache.opt,
         opt_res.minimizer, opt_res.minimum;
         original = opt_res, retcode = opt_ret, stats = stats)
@@ -331,7 +342,10 @@ function SciMLBase.__solve(cache::OptimizationCache{
     cur, state = iterate(cache.data)
 
     function _cb(trace)
-        opt_state = Optimization.OptimizationState(iteration = trace.iteration, u = decompose_trace(trace).metadata["x"], objective = x[1], solver_state = trace)
+        opt_state = Optimization.OptimizationState(iteration = trace.iteration,
+            u = decompose_trace(trace).metadata["x"],
+            objective = x[1],
+            solver_state = trace)
         cb_call = cache.callback(opt_state, x...)
         if !(cb_call isa Bool)
             error("The callback should return a boolean `halt` for whether to stop the optimization process.")
@@ -412,8 +426,9 @@ function SciMLBase.__solve(cache::OptimizationCache{
     opt_res = Optim.optimize(optim_f, optim_fc, cache.u0, cache.opt, opt_args)
     t1 = time()
     opt_ret = Symbol(Optim.converged(opt_res))
-    stats = Optimization.OptimizationStats(; iterations = opt_res.iterations, 
-        time = t1 - t0, fevals = opt_res.f_calls, gevals = opt_res.g_calls, hevals = opt_res.h_calls)
+    stats = Optimization.OptimizationStats(; iterations = opt_res.iterations,
+        time = t1 - t0, fevals = opt_res.f_calls, gevals = opt_res.g_calls,
+        hevals = opt_res.h_calls)
     SciMLBase.build_solution(cache, cache.opt,
         opt_res.minimizer, opt_res.minimum;
         original = opt_res, retcode = opt_ret,

diff --git a/lib/OptimizationOptimisers/src/OptimizationOptimisers.jl b/lib/OptimizationOptimisers/src/OptimizationOptimisers.jl
@@ -67,7 +67,11 @@ function SciMLBase.__solve(cache::OptimizationCache{
         for (i, d) in enumerate(data)
             cache.f.grad(G, θ, d...)
             x = cache.f(θ, cache.p, d...)
-            opt_state = Optimization.OptimizationState(iteration = i, u = θ, objective = x[1], gradient = G, solver_state = state)
+            opt_state = Optimization.OptimizationState(iteration = i,
+                u = θ,
+                objective = x[1],
+                gradient = G,
+                solver_state = state)
             cb_call = cache.callback(opt_state, x...)
             if !(cb_call isa Bool)
                 error("The callback should return a boolean `halt` for whether to stop the optimization process. Please see the `solve` documentation for information.")
@@ -88,7 +92,11 @@ function SciMLBase.__solve(cache::OptimizationCache{
                     x = min_err
                     θ = min_θ
                     cache.f.grad(G, θ, d...)
-                    opt_state = Optimization.OptimizationState(iteration = i, u = θ, objective = x[1], gradient = G, solver_state = state)
+                    opt_state = Optimization.OptimizationState(iteration = i,
+                        u = θ,
+                        objective = x[1],
+                        gradient = G,
+                        solver_state = state)
                     cache.callback(opt_state, x...)
                     break
                 end

diff --git a/lib/OptimizationOptimisers/test/runtests.jl b/lib/OptimizationOptimisers/test/runtests.jl
@@ -61,11 +61,15 @@ using Zygote
 
         prob = OptimizationProblem(optprob, x0, _p)
         function callback(state, l)
-            Optimisers.adjust!(state.solver_state, 0.1/state.iteration)
+            Optimisers.adjust!(state.solver_state, 0.1 / state.iteration)
             return false
         end
-        sol = solve(prob, Optimisers.Adam(0.1), maxiters = 1000, progress = false, callback = callback)
+        sol = solve(prob,
+            Optimisers.Adam(0.1),
+            maxiters = 1000,
+            progress = false,
+            callback = callback)
     end
 
-    @test_throws ArgumentError sol = solve(prob, Optimisers.Adam())
+    @test_throws ArgumentError sol=solve(prob, Optimisers.Adam())
 end
diff --git a/src/stats_state.jl b/src/stats_state.jl
@@ -7,8 +7,9 @@ struct OptimizationStats
     hevals::Int
 end
 
-OptimizationStats(; iterations = 0, time = 0.0, fevals = 0, gevals = 0, hevals = 0) =
+function OptimizationStats(; iterations = 0, time = 0.0, fevals = 0, gevals = 0, hevals = 0)
     OptimizationStats(iterations, time, fevals, gevals, hevals)
+end
 
 struct OptimizationState{X, O, G, H, S}
     iteration::Int
@@ -19,6 +20,7 @@ struct OptimizationState{X, O, G, H, S}
     solver_state::S
 end
 
-OptimizationState(; iteration = 0, u = nothing, objective = nothing,
-        gradient = nothing, hessian = nothing, solver_state = nothing) =
+function OptimizationState(; iteration = 0, u = nothing, objective = nothing,
+        gradient = nothing, hessian = nothing, solver_state = nothing)
     OptimizationState(iteration, u, objective, gradient, hessian, solver_state)
+end
diff --git a/test/diffeqfluxtests.jl b/test/diffeqfluxtests.jl
@@ -35,7 +35,6 @@ end
 
 iter = 0
 callback = function (state, l, pred)
-
     display(l)
 
     # using `remake` to re-create our `prob` with current parameters `p`