Add suggestions from review, fieldnames changed

lib/OptimizationBBO/src/OptimizationBBO.jl

@@ -117,7 +117,7 @@ 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], original = trace)
             cb_call = cache.callback(opt_state, x...)
         end
 

lib/OptimizationCMAEvolutionStrategy/src/OptimizationCMAEvolutionStrategy.jl

@@ -78,7 +78,7 @@ function SciMLBase.__solve(cache::OptimizationCache{
         opt_state = Optimization.OptimizationState(; iteration = length(opt.logger.fmedian),
             u = curr_u,
             objective = opt.logger.fbest[end],
-            solver_state = opt.logger)
+            original = opt.logger)
 
         cb_call = cache.callback(opt_state, x...)
         if !(cb_call isa Bool)

lib/OptimizationEvolutionary/src/OptimizationEvolutionary.jl

@@ -88,7 +88,7 @@ function SciMLBase.__solve(cache::OptimizationCache{
         opt_state = Optimization.OptimizationState(; iteration = decompose_trace(trace).iteration,
             u = curr_u,
             objective = x[1],
-            solver_state = trace)
+            original = trace)
         cb_call = cache.callback(opt_state, trace.value...)
         if !(cb_call isa Bool)
             error("The callback should return a boolean `halt` for whether to stop the optimization process.")

lib/OptimizationFlux/src/OptimizationFlux.jl

@@ -69,7 +69,7 @@ function SciMLBase.__solve(cache::OptimizationCache{
             opt_state = Optimization.OptimizationState(; iteration = i,
                 u = θ,
                 objective = x[1],
-                solver_state = opt)
+                original = opt)
             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 sciml_train documentation for information.")
@@ -92,7 +92,7 @@ function SciMLBase.__solve(cache::OptimizationCache{
                     opt_state = Optimization.OptimizationState(; iteration = i,
                         u = θ,
                         objective = x[1],
-                        solver_state = opt)
+                        original = opt)
                     cache.callback(opt_state, x...)
                     break
                 end

lib/OptimizationOptimJL/src/OptimizationOptimJL.jl

@@ -134,7 +134,7 @@ function SciMLBase.__solve(cache::OptimizationCache{
 
     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)
+        opt_state = Optimization.OptimizationState(iteration = trace.iteration, u = θ, objective = x[1], original = 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.")
@@ -248,7 +248,7 @@ function SciMLBase.__solve(cache::OptimizationCache{
 
     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)
+        opt_state = Optimization.OptimizationState(iteration = trace.iteration, u = θ, objective = x[1], original = 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.")
@@ -331,7 +331,7 @@ 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], original = 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.")

lib/OptimizationOptimisers/src/OptimizationOptimisers.jl

@@ -67,7 +67,7 @@ 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], grad = G, original = 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 +88,7 @@ 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], grad = G, original = state)
                     cache.callback(opt_state, x...)
                     break
                 end

src/stats_state.jl

@@ -34,20 +34,20 @@ Stores the optimization run's state at the current iteration
 and is passed to the callback function as the first argument.
 
 ## Fields
-- `iteration`: current iteration
+- `iter`: current iteration
 - `u`: current solution
 - `objective`: current objective value
 - `gradient`: current gradient
 - `hessian`: current hessian
-- `solver_state`: if the solver has its own state object then it is stored here
+- `original`: if the solver has its own state object then it is stored here
 """
 struct OptimizationState{X, O, G, H, S}
     iteration::Int
     u::X
     objective::O
-    gradient::G
-    hessian::H
-    solver_state::S
+    grad::G
+    hess::H
+    original::S
 end
 
 function OptimizationState(; iteration = 0, u = nothing, objective = nothing,