streamline optimization result methods

Author: Maximilian-Stefan-Ernst

docs/src/developer/optimizer.md

@@ -30,7 +30,6 @@ update_observed(optimizer::SemOptimizerName, observed::SemObserved; kwargs...) =
 ### additional methods
 ############################################################################################
 
-algorithm(optimizer::SemOptimizerName) = optimizer.algorithm
 options(optimizer::SemOptimizerName) = optimizer.options
 ```
 
@@ -68,7 +67,7 @@ The method has to return a `SemFit` object that consists of the minimum of the o
 In addition, you might want to provide methods to access properties of your optimization result:
 
 ```julia
-optimizer(res::MyOptimizationResult) = ...
+algorithm_name(res::MyOptimizationResult) = ...
 n_iterations(res::MyOptimizationResult) = ...
 convergence(res::MyOptimizationResult) = ...
 ```

ext/SEMNLOptExt/NLopt.jl

@@ -122,7 +122,6 @@ SEM.update_observed(optimizer::SemOptimizerNLopt, observed::SemObserved; kwargs.
 ### additional methods
 ############################################################################################
 
-SEM.algorithm(optimizer::SemOptimizerNLopt) = optimizer.algorithm
 local_algorithm(optimizer::SemOptimizerNLopt) = optimizer.local_algorithm
 SEM.options(optimizer::SemOptimizerNLopt) = optimizer.options
 local_options(optimizer::SemOptimizerNLopt) = optimizer.local_options
@@ -134,7 +133,7 @@ struct NLoptResult
     problem::Any
 end
 
-SEM.optimizer(res::NLoptResult) = res.problem.algorithm
+SEM.algorithm_name(res::NLoptResult) = res.problem.algorithm
 SEM.n_iterations(res::NLoptResult) = res.problem.numevals
 SEM.convergence(res::NLoptResult) = res.result[3]
 

ext/SEMProximalOptExt/ProximalAlgorithms.jl

@@ -47,7 +47,12 @@ SEM.update_observed(optimizer::SemOptimizerProximal, observed::SemObserved; kwar
 ### additional methods
 ############################################################################################
 
-SEM.algorithm(optimizer::SemOptimizerProximal) = optimizer.algorithm
+SEM.algorithm_name(res::ProximalResult) = SEM.algorithm_name(res.result[:algorithm])
+SEM.algorithm_name(::ProximalAlgorithms.IterativeAlgorithm{I,H,S,D,K}) where 
+    {I, H, S, D, K} = nameof(I)
+
+SEM.convergence(::ProximalResult) = "No standard convergence criteria for proximal \n algorithms available."
+SEM.n_iterations(res::ProximalResult) = res.result[:iterations]
 
 ############################################################################
 ### Pretty Printing

src/frontend/fit/SemFit.jl

@@ -13,7 +13,7 @@ Fitted structural equation model.
 - `model(::SemFit)`
 - `optimization_result(::SemFit)`
 
-- `optimizer(::SemFit)` -> optimization algorithm
+- `algorithm_name(::SemFit)` -> optimization algorithm
 - `n_iterations(::SemFit)` -> number of iterations
 - `convergence(::SemFit)` -> convergence properties
 """
@@ -63,7 +63,7 @@ model(sem_fit::SemFit) = sem_fit.model
 optimization_result(sem_fit::SemFit) = sem_fit.optimization_result
 
 # optimizer properties
+algorithm_name(sem_fit::SemFit) = algorithm_name(sem_fit.optimization_result)
 optimizer_engine(sem_fit::SemFit) = optimizer_engine(sem_fit.optimizer)
-optimizer(sem_fit::SemFit) = optimizer(optimization_result(sem_fit))
 n_iterations(sem_fit::SemFit) = n_iterations(optimization_result(sem_fit))
 convergence(sem_fit::SemFit) = convergence(optimization_result(sem_fit))

src/frontend/fit/summary.jl

@@ -7,7 +7,8 @@ function details(sem_fit::SemFit; show_fitmeasures = false, color = :light_cyan,
         color = color,
     )
     print("\n")
-    println("Optimization algorithm:      $(optimizer(sem_fit))")
+    println("Optimization engine:         $(optimizer_engine(sem_fit))")
+    println("Optimization algorithm:      $(algorithm_name(sem_fit))")
     println("Convergence:                 $(convergence(sem_fit))")
     println("No. iterations/evaluations:  $(n_iterations(sem_fit))")
     print("\n")

src/optimizer/optim.jl

@@ -73,10 +73,9 @@ update_observed(optimizer::SemOptimizerOptim, observed::SemObserved; kwargs...)
 ### additional methods
 ############################################################################################
 
-algorithm(optimizer::SemOptimizerOptim) = optimizer.algorithm
 options(optimizer::SemOptimizerOptim) = optimizer.options
 
-optimizer(res::Optim.MultivariateOptimizationResults) = Optim.summary(res)
+algorithm_name(res::Optim.MultivariateOptimizationResults) = Optim.summary(res)
 n_iterations(res::Optim.MultivariateOptimizationResults) = Optim.iterations(res)
 convergence(res::Optim.MultivariateOptimizationResults) = Optim.converged(res)