Use package extensions

.cirrus.yml

@@ -2,7 +2,7 @@ task:
   matrix:
     - name: FreeBSD
       freebsd_instance:
-        image_family: freebsd-13-3
+        image_family: freebsd-14-2
       env:
         matrix:
           - JULIA_VERSION: 1

Project.toml

@@ -1,37 +1,43 @@
 name = "ExpressionTreeForge"
 uuid = "93090adf-0e31-445f-8c8f-44d91f61d7ad"
-authors = ["raynaudp "]
+authors = ["raynaudp"]
 version = "0.3.0"
 
 [deps]
-ADNLPModels = "54578032-b7ea-4c30-94aa-7cbd1cce6c9a"
 ForwardDiff = "f6369f11-7733-5829-9624-2563aa707210"
-JuMP = "4076af6c-e467-56ae-b986-b466b2749572"
 LinearAlgebra = "37e2e46d-f89d-539d-b4ee-838fcccc9c8e"
 MathOptInterface = "b8f27783-ece8-5eb3-8dc8-9495eed66fee"
-NLPModelsJuMP = "792afdf1-32c1-5681-94e0-d7bf7a5df49e"
 ReverseDiff = "37e2e3b7-166d-5795-8a7a-e32c996b4267"
 SparseArrays = "2f01184e-e22b-5df5-ae63-d93ebab69eaf"
+
+[weakdeps]
+ADNLPModels = "54578032-b7ea-4c30-94aa-7cbd1cce6c9a"
+JuMP = "4076af6c-e467-56ae-b986-b466b2749572"
+NLPModelsJuMP = "792afdf1-32c1-5681-94e0-d7bf7a5df49e"
 Symbolics = "0c5d862f-8b57-4792-8d23-62f2024744c7"
 
+[extensions]
+ExpressionTreeForgeJuMPExt = "JuMP"
+ExpressionTreeForgeADNLPModelsExt = ["ADNLPModels", "Symbolics"]
+ExpressionTreeForgeNLPModelsJuMPExt = "NLPModelsJuMP"
+
 [compat]
 ADNLPModels = "0.8"
 ForwardDiff = "0.10,1"
+LinearAlgebra = "1.10"
 JuMP = "1"
 MathOptInterface = "1"
 NLPModelsJuMP = "0.13"
+OptimizationProblems = "0.9"
 ReverseDiff = "1.14"
-Symbolics = "5,6"
-julia = "^1.10.0"
+SparseArrays = "1.10"
+Symbolics = "6"
+Test = "1.10"
+julia = "1.10"
 
 [extras]
-ADNLPModels = "54578032-b7ea-4c30-94aa-7cbd1cce6c9a"
-MathOptInterface = "b8f27783-ece8-5eb3-8dc8-9495eed66fee"
-Symbolics = "0c5d862f-8b57-4792-8d23-62f2024744c7"
-NLPModelsJuMP = "792afdf1-32c1-5681-94e0-d7bf7a5df49e"
 OptimizationProblems = "5049e819-d29b-5fba-b941-0eee7e64c1c6"
-SparseArrays = "2f01184e-e22b-5df5-ae63-d93ebab69eaf"
 Test = "8dfed614-e22c-5e08-85e1-65c5234f0b40"
 
 [targets]
-test = ["Test", "SparseArrays", "MathOptInterface", "Symbolics", "ADNLPModels", "OptimizationProblems", "NLPModelsJuMP"]
+test = ["ADNLPModels", "OptimizationProblems", "NLPModelsJuMP", "JuMP", "Symbolics", "Test"]

ext/ExpressionTreeForgeADNLPModelsExt.jl

@@ -0,0 +1,16 @@
+module ExpressionTreeForgeADNLPModelsExt
+
+import ExpressionTreeForge
+import ADNLPModels
+import Symbolics
+
+function ExpressionTreeForge.get_expression_tree(adnlp::ADNLPModels.ADNLPModel)
+  n = adnlp.meta.nvar
+  Symbolics.@variables x[1:n]
+  fun = adnlp.f(x)
+  obj_Expr = Symbolics._toexpr(fun)
+  expr_tree = ExpressionTreeForge.transform_to_expr_tree(obj_Expr)::ExpressionTreeForge.Type_expr_tree
+  return expr_tree
+end
+
+end

ext/ExpressionTreeForgeJuMPExt.jl

@@ -0,0 +1,18 @@
+module ExpressionTreeForgeJuMPExt
+
+import ExpressionTreeForge
+import JuMP
+import MathOptInterface as MOI
+
+function ExpressionTreeForge.get_expression_tree(model::JuMP.Model)
+  nlp = JuMP.nonlinear_model(model; force = true)
+  evaluator = MOI.Nonlinear.Evaluator(nlp)
+  F = MOI.get(model, MOI.ObjectiveFunctionType())
+  MOI.get(model, MOI.ObjectiveFunction{F}())
+  if F <: MOI.ScalarNonlinearFunction
+    MOI.Nonlinear.set_objective(nlp, MOI.get(model, MOI.ObjectiveFunction{F}()))
+  end
+  return ExpressionTreeForge.get_expression_tree(evaluator)
+end
+
+end

ext/ExpressionTreeForgeNLPModelsJuMPExt.jl

@@ -0,0 +1,8 @@
+module ExpressionTreeForgeNLPModelsJuMPExt
+
+import ExpressionTreeForge
+import NLPModelsJuMP
+
+ExpressionTreeForge.get_expression_tree(nlp::NLPModelsJuMP.MathOptNLPModel) = ExpressionTreeForge.get_expression_tree(nlp.eval)
+
+end

src/ExpressionTreeForge.jl

@@ -1,5 +1,6 @@
 module ExpressionTreeForge
 
+include("get_expression_tree.jl")
 include("type_expr/ordered_include.jl")
 include("node_expr_tree/ordered_include.jl")
 include("tree/ordered_include.jl")
@@ -12,8 +13,6 @@ using .M_bound_propagations, .M_convexity_detection
 
 include("export.jl")
 
-include("NLPSupport/ordered_include.jl")
-
 export create_bounds_tree, get_bounds, set_bounds!
 export Type_node, Complete_expr_tree, Pre_compiled_tree, Pre_n_compiled_tree, Type_calculus_tree
 export concave_type, constant_type, convex_type, linear_type, not_treated_type, unknown_type

src/get_expression_tree.jl

@@ -0,0 +1,23 @@
+import MathOptInterface as MOI
+
+"""
+    expr = get_expression_tree(monlp::MathOptNLPModel)
+    expr = get_expression_tree(adnlp::ADNLPModel)
+    expr = get_expression_tree(model::JuMP.Model)
+    expr = get_expression_tree(evaluator::MOI.Nonlinear.Evaluator)
+
+Return the objective function as a `Type_expr_tree` for: a `MathOptNLPModel`, a `ADNLPModel`, a `JuMP.Model` or a `MOI.Nonlinear.Evaluator`.
+"""
+function get_expression_tree end
+
+function get_expression_tree(evaluator::MOI.Nonlinear.Evaluator)
+  MOI.initialize(evaluator, [:ExprGraph])
+  obj_Expr = MOI.objective_expr(evaluator)::Expr
+  expr_tree = ExpressionTreeForge.transform_to_expr_tree(obj_Expr)::ExpressionTreeForge.Type_expr_tree
+  return expr_tree
+end
+
+function ExpressionTreeForge.get_expression_tree(model::MOI.Nonlinear.Model)
+  evaluator = MOI.Nonlinear.Evaluator(model)
+  return ExpressionTreeForge.get_expression_tree(evaluator)
+end

test/runtests.jl

@@ -1,5 +1,6 @@
 using LinearAlgebra, SparseArrays, Test
-using JuMP, MathOptInterface, Symbolics
+using JuMP, MathOptInterface
+using ADNLPModels, Symbolics
 using ExpressionTreeForge
 
 const MOI = MathOptInterface