Add default printing for show(io, ::ModelLike)

odow commented 2 months ago

Closes #2504

[x] Still needs to fix tests and some finessing.
[x] Let's see how many tests depend on show: https://github.com/jump-dev/MathOptInterface.jl/actions/runs/9218665500

odow commented 2 months ago

Here's where I'm heading:

julia> model = MOI.Utilities.Model{Float64}()
An empty MOIU.Model{Float64}

julia> MOI.Utilities.loadfromstring!(model, """
       variables: x, y
       minobjective: 2.0 * x
       c1: x >= 0.0
       c2: 1.0 * x + 2.0 * y <= 3.0
       c2: 1.0 * x + 3.0 * y <= 2.0
       """)

julia> MOI.Utilities.CachingOptimizer(model, MOI.Utilities.Model{Float64}())
A MOI.Utilities.CachingOptimizer:
├ state
│ └ EMPTY_OPTIMIZER
├ mode
│ └ AUTOMATIC
├ model_cache :: MOIU.Model{Float64}
  A MOIU.Model{Float64}
  ├ ObjectiveSense
  │ └ MIN_SENSE
  ├ ObjectiveFunctionType
  │ └ MOI.ScalarAffineFunction{Float64}
  ├ NumberOfVariables
  │ └ 2
  └ NumberOfConstraints
    ├ MOI.ScalarAffineFunction{Float64} in MOI.LessThan{Float64}: 2
    └ MOI.VariableIndex in MOI.GreaterThan{Float64}: 1
└ optimizer :: MOIU.Model{Float64}
  An empty MOIU.Model{Float64}

odow commented 2 months ago

I think I like this:

julia> include("test/Bridges/sdpa_models.jl")

julia> model = MOI.Utilities.CachingOptimizer(
           MOI.Utilities.Model{Float64}(),
           MOI.Bridges.full_bridge_optimizer(StandardSDPAModel{Float64}(), Float64),
       )
A MOI.Utilities.CachingOptimizer:
├ state
│ └ EMPTY_OPTIMIZER
├ mode
│ └ AUTOMATIC
├ model_cache
│ An empty MOIU.Model{Float64}
└ optimizer
  A MOIB.LazyBridgeOptimizer{MOIU.GenericModel{Float64, MOIU.ObjectiveContainer{Float64}, MOIU.VariablesContainer{Float64}, StandardSDPAModelFunctionConstraints{Float64}}}
  ├ Variable bridges
  │ └ none
  ├ Constraint bridges
  │ └ none
  ├ Objective bridges
  │ └ none
  └ model
    An empty MOIU.GenericModel{Float64, MOIU.ObjectiveContainer{Float64}, MOIU.VariablesContainer{Float64}, StandardSDPAModelFunctionConstraints{Float64}}

julia> x = MOI.add_variable(model)
MOI.VariableIndex(1)

julia> MOI.add_constraint(model, 2.0 * x, MOI.LessThan(3.0))
MathOptInterface.ConstraintIndex{MathOptInterface.ScalarAffineFunction{Float64}, MathOptInterface.LessThan{Float64}}(1)

julia> MOI.Utilities.attach_optimizer(model)

julia> model
A MOI.Utilities.CachingOptimizer:
├ state
│ └ ATTACHED_OPTIMIZER
├ mode
│ └ AUTOMATIC
├ model_cache
│ A MOIU.Model{Float64}
│ ├ ObjectiveSense
│ │ └ FEASIBILITY_SENSE
│ ├ ObjectiveFunctionType
│ │ └ MOI.ScalarAffineFunction{Float64}
│ ├ NumberOfVariables
│ │ └ 1
│ └ NumberOfConstraints
│   └ MOI.ScalarAffineFunction{Float64} in MOI.LessThan{Float64}: 1
└ optimizer
  A MOIB.LazyBridgeOptimizer{MOIU.GenericModel{Float64, MOIU.ObjectiveContainer{Float64}, MOIU.VariablesContainer{Float64}, StandardSDPAModelFunctionConstraints{Float64}}}
  ├ Variable bridges
  │ ├ MOIB.Variable.FreeBridge{Float64}
  │ └ MOIB.Variable.VectorizeBridge{Float64, MOI.Nonnegatives}
  ├ Constraint bridges
  │ ├ MOIB.Constraint.LessToGreaterBridge{Float64, MOI.ScalarAffineFunction{Float64}, MOI.ScalarAffineFunction{Float64}}
  │ └ MOIB.Constraint.ScalarSlackBridge{Float64, MOI.ScalarAffineFunction{Float64}, MOI.GreaterThan{Float64}}
  ├ Objective bridges
  │ └ none
  └ model
    A MOIU.GenericModel{Float64, MOIU.ObjectiveContainer{Float64}, MOIU.VariablesContainer{Float64}, StandardSDPAModelFunctionConstraints{Float64}}
    ├ ObjectiveSense
    │ └ FEASIBILITY_SENSE
    ├ ObjectiveFunctionType
    │ └ MOI.ScalarAffineFunction{Float64}
    ├ NumberOfVariables
    │ └ 3
    └ NumberOfConstraints
      ├ MOI.ScalarAffineFunction{Float64} in MOI.EqualTo{Float64}: 1
      └ MOI.VectorOfVariables in MOI.Nonnegatives: 2

odow commented 2 months ago

So this is actually super useful for debugging.

Cribbing from a recent post on discourse:

julia> using JuMP

julia> import HiGHS

julia> import MultiObjectiveAlgorithms as MOA

julia> model = Model(() -> MOA.Optimizer(HiGHS.Optimizer));

julia> set_silent(model)

julia> set_attribute(model, MOA.Algorithm(), MOA.Hierarchical())

julia> @variables(model, begin
           lb[i] <= z[i in 1:length(lb)] <= ub[i], Int
           d >= 0
       end);

julia> @constraints(model, begin
           z - lb .- d >= 0
           z - ub .+ d <= 0
           sum(z) <= 500
       end);

julia> @objective(model, Max, [sum(z), d]);

julia> optimize!(model)

julia> backend(model)
A MOI.Utilities.CachingOptimizer:
├ state
│ └ ATTACHED_OPTIMIZER
├ mode
│ └ AUTOMATIC
├ model_cache
│ A MOIU.UniversalFallback{MOIU.Model{Float64}}
│ ├ ObjectiveSense
│ │ └ MAX_SENSE
│ ├ ObjectiveFunctionType
│ │ └ MOI.VectorAffineFunction{Float64}
│ ├ NumberOfVariables
│ │ └ 11
│ └ NumberOfConstraints
│   ├ MOI.ScalarAffineFunction{Float64} in MOI.LessThan{Float64}: 1
│   ├ MOI.VectorAffineFunction{Float64} in MOI.Nonnegatives: 1
│   ├ MOI.VectorAffineFunction{Float64} in MOI.Nonpositives: 1
│   ├ MOI.VariableIndex in MOI.GreaterThan{Float64}: 11
│   ├ MOI.VariableIndex in MOI.LessThan{Float64}: 10
│   └ MOI.VariableIndex in MOI.Integer: 10
└ optimizer
  A MOIB.LazyBridgeOptimizer{MultiObjectiveAlgorithms.Optimizer}
  ├ Variable bridges
  │ └ none
  ├ Constraint bridges
  │ ├ MOIB.Constraint.ScalarizeBridge{Float64, MOI.ScalarAffineFunction{Float64}, MOI.GreaterThan{Float64}}
  │ └ MOIB.Constraint.ScalarizeBridge{Float64, MOI.ScalarAffineFunction{Float64}, MOI.LessThan{Float64}}
  ├ Objective bridges
  │ └ none
  └ model
    A MultiObjectiveAlgorithms.Optimizer
    ├ ObjectiveSense
    │ └ MAX_SENSE
    ├ ObjectiveFunctionType
    │ └ MOI.VectorAffineFunction{Float64}
    ├ NumberOfVariables
    │ └ 11
    └ NumberOfConstraints
      ├ MOI.ScalarAffineFunction{Float64} in MOI.GreaterThan{Float64}: 10
      ├ MOI.ScalarAffineFunction{Float64} in MOI.LessThan{Float64}: 11
      ├ MOI.VariableIndex in MOI.GreaterThan{Float64}: 11
      ├ MOI.VariableIndex in MOI.LessThan{Float64}: 10
      └ MOI.VariableIndex in MOI.Integer: 10

ericphanson commented 2 months ago

This looks nice! I wonder if it could be a bit more compact in cases when there's only 1 child? I've found too much scrolling in the REPL can make it harder to understand a printout. e.g.

A MOI.Utilities.CachingOptimizer:
├ state: ATTACHED_OPTIMIZER
├ mode: AUTOMATIC
├ model_cache: MOIU.UniversalFallback{MOIU.Model{Float64}}
│ ├ ObjectiveSense: MAX_SENSE
│ ├ ObjectiveFunctionType: MOI.VectorAffineFunction{Float64}
│ ├ NumberOfVariables: 11
│ └ NumberOfConstraints: 34
│   ├ MOI.ScalarAffineFunction{Float64} in MOI.LessThan{Float64}: 1
│   ├ MOI.VectorAffineFunction{Float64} in MOI.Nonnegatives: 1
│   ├ MOI.VectorAffineFunction{Float64} in MOI.Nonpositives: 1
│   ├ MOI.VariableIndex in MOI.GreaterThan{Float64}: 11
│   ├ MOI.VariableIndex in MOI.LessThan{Float64}: 10
│   └ MOI.VariableIndex in MOI.Integer: 10
└ optimizer: MOIB.LazyBridgeOptimizer{MultiObjectiveAlgorithms.Optimizer}
  ├ Variable bridges: none
  ├ Constraint bridges
  │ ├ MOIB.Constraint.ScalarizeBridge{Float64, MOI.ScalarAffineFunction{Float64}, MOI.GreaterThan{Float64}}
  │ └ MOIB.Constraint.ScalarizeBridge{Float64, MOI.ScalarAffineFunction{Float64}, MOI.LessThan{Float64}}
  ├ Objective bridges: none
  └ model: MultiObjectiveAlgorithms.Optimizer
    ├ ObjectiveSense: MAX_SENSE
    ├ ObjectiveFunctionType: MOI.VectorAffineFunction{Float64}
    ├ NumberOfVariables: 11
    └ NumberOfConstraints: 52
      ├ MOI.ScalarAffineFunction{Float64} in MOI.GreaterThan{Float64}: 10
      ├ MOI.ScalarAffineFunction{Float64} in MOI.LessThan{Float64}: 11
      ├ MOI.VariableIndex in MOI.GreaterThan{Float64}: 11
      ├ MOI.VariableIndex in MOI.LessThan{Float64}: 10
      └ MOI.VariableIndex in MOI.Integer: 10

This is kinda already done for contraints, like MOI.ScalarAffineFunction{Float64} in MOI.GreaterThan{Float64}: 10, so I think it could work also for NumberOfVariables, ObjectiveSense, ObjectiveFunctionType, state and mode.

I think it would also make sense for nones in things like Variable bridges, Constraint bridges.

I also tweaked it so instead of A MOIU.UniversalFallback{MOIU.Model{Float64}} on the following line, I removed the A and put it after a colon. To me the "A" was a bit confusing (I know it's the indefinite article, but the rest isn't really sentences so it's not clear if it means like some kind of "type A" vs "type B" or something). And IMO it reads a little better having it on the same line. I did the same for optimizer: MOIB.LazyBridgeOptimizer{MultiObjectiveAlgorithms.Optimizer} and model: MultiObjectiveAlgorithms.Optimizer.

Lastly I think it could also be nice to have NumberOfConstraints print a total, like I added here.

odow commented 2 months ago

@ericphanson done

odow commented 2 months ago

It's now:

julia> using JuMP

julia> import HiGHS

julia> import MultiObjectiveAlgorithms as MOA

julia> model = Model(() -> MOA.Optimizer(HiGHS.Optimizer));

julia> set_silent(model)

julia> set_attribute(model, MOA.Algorithm(), MOA.Hierarchical())

julia> lb = ones(10); ub = fill(10, 10);

julia> @variables(model, begin
           lb[i] <= z[i in 1:length(lb)] <= ub[i], Int
           d >= 0
       end);

julia> @constraints(model, begin
           z - lb .- d >= 0
           z - ub .+ d <= 0
           sum(z) <= 500
       end);

julia> @objective(model, Max, [sum(z), d]);

julia> optimize!(model)

julia> backend(model)
MOIU.CachingOptimizer
├ state: ATTACHED_OPTIMIZER
├ mode: AUTOMATIC
├ model_cache: MOIU.UniversalFallback{MOIU.Model{Float64}}
│ ├ ObjectiveSense: MAX_SENSE
│ ├ ObjectiveFunctionType: MOI.VectorAffineFunction{Float64}
│ ├ NumberOfVariables: 11
│ └ NumberOfConstraints: 34
│   ├ MOI.ScalarAffineFunction{Float64} in MOI.LessThan{Float64}: 1
│   ├ MOI.VectorAffineFunction{Float64} in MOI.Nonnegatives: 1
│   ├ MOI.VectorAffineFunction{Float64} in MOI.Nonpositives: 1
│   ├ MOI.VariableIndex in MOI.GreaterThan{Float64}: 11
│   ├ MOI.VariableIndex in MOI.LessThan{Float64}: 10
│   └ MOI.VariableIndex in MOI.Integer: 10
└ optimizer:   MOIB.LazyBridgeOptimizer{MultiObjectiveAlgorithms.Optimizer}
  ├ Variable bridges: none
  ├ Constraint bridges:
  │ ├ MOIB.Constraint.ScalarizeBridge{Float64, MOI.ScalarAffineFunction{Float64}, MOI.GreaterThan{Float64}}
  │ └ MOIB.Constraint.ScalarizeBridge{Float64, MOI.ScalarAffineFunction{Float64}, MOI.LessThan{Float64}}
  ├ Objective bridges: none
  └ model: MultiObjectiveAlgorithms.Optimizer
    ├ ObjectiveSense: MAX_SENSE
    ├ ObjectiveFunctionType: MOI.VectorAffineFunction{Float64}
    ├ NumberOfVariables: 11
    └ NumberOfConstraints: 52
      ├ MOI.ScalarAffineFunction{Float64} in MOI.GreaterThan{Float64}: 10
      ├ MOI.ScalarAffineFunction{Float64} in MOI.LessThan{Float64}: 11
      ├ MOI.VariableIndex in MOI.GreaterThan{Float64}: 11
      ├ MOI.VariableIndex in MOI.LessThan{Float64}: 10
      └ MOI.VariableIndex in MOI.Integer: 10

odow commented 2 months ago

Here's a problem;

julia> using JuMP, SCS

julia> model = Model(SCS.Optimizer);

julia> set_silent(model)

julia> @variable(model, x[1:2, 1:2] >= 0, PSD);

julia> @constraint(model, sum(x) == 1);

julia> optimize!(model)

julia> backend(model)
MOIU.CachingOptimizer
├ state: ATTACHED_OPTIMIZER
├ mode: AUTOMATIC
├ model_cache: MOIU.UniversalFallback{MOIU.Model{Float64}}
│ ├ ObjectiveSense: FEASIBILITY_SENSE
│ ├ ObjectiveFunctionType: MOI.ScalarAffineFunction{Float64}
│ ├ NumberOfVariables: 3
│ └ NumberOfConstraints: 5
│   ├ MOI.ScalarAffineFunction{Float64} in MOI.EqualTo{Float64}: 1
│   ├ MOI.VectorOfVariables in MOI.PositiveSemidefiniteConeTriangle: 1
│   └ MOI.VariableIndex in MOI.GreaterThan{Float64}: 3
└ optimizer:   MOIB.LazyBridgeOptimizer{MOIU.CachingOptimizer{SCS.Optimizer, MOIU.UniversalFallback{MOIU.Model{Float64}}}}
  ├ Variable bridges: none
  ├ Constraint bridges:
  │ ├ MOIB.Constraint.SetDotScalingBridge{Float64, MOI.PositiveSemidefiniteConeTriangle, MOI.VectorAffineFunction{Float64}, MOI.VectorOfVariables}
  │ ├ MOIB.Constraint.VectorizeBridge{Float64, MOI.VectorAffineFunction{Float64}, MOI.Nonnegatives, MOI.VariableIndex}
  │ ├ MOIB.Constraint.VectorizeBridge{Float64, MOI.VectorAffineFunction{Float64}, MOI.Zeros, MOI.ScalarAffineFunction{Float64}}
  │ └ SCS.ScaledPSDConeBridge{Float64, MOI.VectorAffineFunction{Float64}}
  ├ Objective bridges: none
  └ model:     MOIU.CachingOptimizer
    ├ state: EMPTY_OPTIMIZER
    ├ mode: AUTOMATIC
    ├ model_cache: MOIU.UniversalFallback{MOIU.Model{Float64}}
    │ ├ ObjectiveSense: FEASIBILITY_SENSE
    │ ├ ObjectiveFunctionType: MOI.ScalarAffineFunction{Float64}
    │ ├ NumberOfVariables: 3
    │ └ NumberOfConstraints: 5
    │   ├ MOI.VectorAffineFunction{Float64} in MOI.Zeros: 1
    │   ├ MOI.VectorAffineFunction{Float64} in MOI.Nonnegatives: 3
    │   └ MOI.VectorAffineFunction{Float64} in SCS.ScaledPSDCone: 1
    └ optimizer: SCS.Optimizer
      ├ ObjectiveSense: FEASIBILITY_SENSE
      ├ ObjectiveFunctionType: ?
      ├ NumberOfVariables: ?
      └ NumberOfConstraints: 0

SCS doesn't support getting various attributes, so there is some weirdness. We could fix that though.

blegat commented 2 months ago

This is very nice, I like it ! It makes the MOI layers much less obscure

odow commented 2 months ago

Now it's

julia> backend(model)
MOIU.CachingOptimizer
├ state: ATTACHED_OPTIMIZER
├ mode: AUTOMATIC
├ model_cache: MOIU.UniversalFallback{MOIU.Model{Float64}}
│ ├ ObjectiveSense: FEASIBILITY_SENSE
│ ├ ObjectiveFunctionType: MOI.ScalarAffineFunction{Float64}
│ ├ NumberOfVariables: 3
│ └ NumberOfConstraints: 5
│   ├ MOI.ScalarAffineFunction{Float64} in MOI.EqualTo{Float64}: 1
│   ├ MOI.VectorOfVariables in MOI.PositiveSemidefiniteConeTriangle: 1
│   └ MOI.VariableIndex in MOI.GreaterThan{Float64}: 3
└ optimizer: MOIB.LazyBridgeOptimizer{MOIU.CachingOptimizer{SCS.Optimizer, MOIU.UniversalFallback{MOIU.Model{Float64}}}}
  ├ Variable bridges: none
  ├ Constraint bridges:
  │ ├ MOIB.Constraint.SetDotScalingBridge{Float64, MOI.PositiveSemidefiniteConeTriangle, MOI.VectorAffineFunction{Float64}, MOI.VectorOfVariables}
  │ ├ MOIB.Constraint.VectorizeBridge{Float64, MOI.VectorAffineFunction{Float64}, MOI.Nonnegatives, MOI.VariableIndex}
  │ ├ MOIB.Constraint.VectorizeBridge{Float64, MOI.VectorAffineFunction{Float64}, MOI.Zeros, MOI.ScalarAffineFunction{Float64}}
  │ └ SCS.ScaledPSDConeBridge{Float64, MOI.VectorAffineFunction{Float64}}
  ├ Objective bridges: none
  └ model: MOIU.CachingOptimizer
    ├ state: EMPTY_OPTIMIZER
    ├ mode: AUTOMATIC
    ├ model_cache: MOIU.UniversalFallback{MOIU.Model{Float64}}
    │ ├ ObjectiveSense: FEASIBILITY_SENSE
    │ ├ ObjectiveFunctionType: MOI.ScalarAffineFunction{Float64}
    │ ├ NumberOfVariables: 3
    │ └ NumberOfConstraints: 5
    │   ├ MOI.VectorAffineFunction{Float64} in MOI.Zeros: 1
    │   ├ MOI.VectorAffineFunction{Float64} in MOI.Nonnegatives: 3
    │   └ MOI.VectorAffineFunction{Float64} in SCS.ScaledPSDCone: 1
    └ optimizer: SCS.Optimizer
      ├ ObjectiveSense: unknown
      ├ ObjectiveFunctionType: unknown
      ├ NumberOfVariables: unknown
      └ NumberOfConstraints: unknown

ericphanson commented 2 months ago

looks great!

odow commented 2 months ago

Thoughts @blegat?

blegat commented 2 months ago

Maybe we should add a test printing an empty model that every solver runs. It should be safe with the _try_catch but you never know, a solver could also segfault ^^

odow commented 2 months ago

I think this is a really nice quality of life improvement. Don't know why we didn't do this earlier.

jump-dev / MathOptInterface.jl

Add default printing for show(io, ::ModelLike) #2505