Remove obsolete methods

deps/build.jl

@@ -5,7 +5,7 @@ function install_miplearn()
     Conda.update()
     pip = joinpath(dirname(pyimport("sys").executable), "pip")
     isfile(pip) || error("$pip: invalid path")
-    run(`$pip install miplearn==0.2.0.dev5`)
+    run(`$pip install miplearn==0.2.0.dev6`)
 end
 
 install_miplearn()

src/MIPLearn.jl

@@ -15,11 +15,9 @@ export @category
 miplearn = pyimport("miplearn")
 
 include("utils/log.jl")
-include("utils/pycall.jl")
 include("modeling/jump_instance.jl")
 include("modeling/jump_solver.jl")
 include("modeling/learning_solver.jl")
 include("modeling/macros.jl")
-include("problems/knapsack.jl")
 
 end # module

src/modeling/jump_solver.jl

@@ -22,12 +22,12 @@ end
 
 
 """
-    optimize_and_capture_output!(model; tee=tee)
+    _optimize_and_capture_output!(model; tee=tee)
 
 Optimizes a given JuMP model while capturing the solver log, then returns that log.
 If tee=true, prints the solver log to the standard output as the optimization takes place.
 """
-function optimize_and_capture_output!(model; tee::Bool=false)
+function _optimize_and_capture_output!(model; tee::Bool=false)
     logname = tempname()
     logfile = open(logname, "w")
     redirect_stdout(logfile) do 
@@ -44,71 +44,7 @@ function optimize_and_capture_output!(model; tee::Bool=false)
 end
 
 
-function solve(
-    data::JuMPSolverData;
-    tee::Bool=false,
-    iteration_cb=nothing,
-)
-    instance, model = data.instance, data.model
-    wallclock_time = 0
-    log = ""
-    while true
-        log *= optimize_and_capture_output!(model, tee=tee)
-        wallclock_time += JuMP.solve_time(model)
-        if iteration_cb !== nothing
-            iteration_cb() || break
-        else
-            break
-        end
-    end
-    update_solution!(data)
-    primal_bound = JuMP.objective_value(model)
-    dual_bound = JuMP.objective_bound(model)
-    if JuMP.objective_sense(model) == MOI.MIN_SENSE
-        sense = "min"
-        lower_bound = dual_bound
-        upper_bound = primal_bound
-    else
-        sense = "max"
-        lower_bound = primal_bound
-        upper_bound = dual_bound
-    end
-    return miplearn.solvers.internal.MIPSolveStats(
-        mip_lower_bound=lower_bound,
-        mip_upper_bound=upper_bound,
-        mip_sense=sense,
-        mip_wallclock_time=wallclock_time,
-        mip_nodes=1,
-        mip_log=log,
-        mip_warm_start_value=nothing,
-    )
-end
-
-
-function solve_lp(data::JuMPSolverData; tee::Bool=false)
-    model, bin_vars = data.model, data.bin_vars
-    for var in bin_vars
-        JuMP.unset_binary(var)
-        JuMP.set_upper_bound(var, 1.0)
-        JuMP.set_lower_bound(var, 0.0)
-    end
-    wallclock_time = @elapsed begin
-        log = optimize_and_capture_output!(model, tee=tee)
-    end
-    update_solution!(data)
-    obj_value = JuMP.objective_value(model)
-    for var in bin_vars
-        JuMP.set_binary(var)
-    end
-    return miplearn.solvers.internal.LPSolveStats(
-        lp_value=obj_value,
-        lp_log=log,
-        lp_wallclock_time=wallclock_time,
-    )
-end
-
-
-function update_solution!(data::JuMPSolverData)
+function _update_solution!(data::JuMPSolverData)
     vars = JuMP.all_variables(data.model)
     data.solution = [JuMP.value(var) for var in vars]
 
@@ -144,6 +80,70 @@ function update_solution!(data::JuMPSolverData)
 end
 
 
+function solve(
+    data::JuMPSolverData;
+    tee::Bool=false,
+    iteration_cb=nothing,
+)
+    instance, model = data.instance, data.model
+    wallclock_time = 0
+    log = ""
+    while true
+        log *= _optimize_and_capture_output!(model, tee=tee)
+        wallclock_time += JuMP.solve_time(model)
+        if iteration_cb !== nothing
+            iteration_cb() || break
+        else
+            break
+        end
+    end
+    _update_solution!(data)
+    primal_bound = JuMP.objective_value(model)
+    dual_bound = JuMP.objective_bound(model)
+    if JuMP.objective_sense(model) == MOI.MIN_SENSE
+        sense = "min"
+        lower_bound = dual_bound
+        upper_bound = primal_bound
+    else
+        sense = "max"
+        lower_bound = primal_bound
+        upper_bound = dual_bound
+    end
+    return miplearn.solvers.internal.MIPSolveStats(
+        mip_lower_bound=lower_bound,
+        mip_upper_bound=upper_bound,
+        mip_sense=sense,
+        mip_wallclock_time=wallclock_time,
+        mip_nodes=1,
+        mip_log=log,
+        mip_warm_start_value=nothing,
+    )
+end
+
+
+function solve_lp(data::JuMPSolverData; tee::Bool=false)
+    model, bin_vars = data.model, data.bin_vars
+    for var in bin_vars
+        JuMP.unset_binary(var)
+        JuMP.set_upper_bound(var, 1.0)
+        JuMP.set_lower_bound(var, 0.0)
+    end
+    wallclock_time = @elapsed begin
+        log = _optimize_and_capture_output!(model, tee=tee)
+    end
+    _update_solution!(data)
+    obj_value = JuMP.objective_value(model)
+    for var in bin_vars
+        JuMP.set_binary(var)
+    end
+    return miplearn.solvers.internal.LPSolveStats(
+        lp_value=obj_value,
+        lp_log=log,
+        lp_wallclock_time=wallclock_time,
+    )
+end
+
+
 function set_instance!(data::JuMPSolverData, instance, model)
     data.instance = instance
     data.model = model
@@ -188,39 +188,11 @@ function set_warm_start!(data::JuMPSolverData, solution)
 end
 
 
-function get_variable_names(data::JuMPSolverData)
-    return [JuMP.name(var) for var in JuMP.all_variables(data.model)]
-end
-
-
 function is_infeasible(data::JuMPSolverData)
     return JuMP.termination_status(data.model) == MOI.INFEASIBLE
 end
 
 
-function get_constraint_ids(data::JuMPSolverData)
-    return [cname for cname in keys(data.cname_to_constr)]
-end
-
-
-function get_constraint_rhs(data::JuMPSolverData, cname)
-    constr = data.cname_to_constr[cname]
-    return get_constraint_rhs(constr)
-end
-
-
-function get_constraint_lhs(data::JuMPSolverData, cname)
-    constr = data.cname_to_constr[cname]
-    return get_constraint_lhs(constr)
-end
-
-
-function get_constraint_sense(data::JuMPSolverData, cname)
-    constr = data.cname_to_constr[cname]
-    return get_constraint_sense(constr)
-end
-
-
 function get_variables(
     data::JuMPSolverData;
     with_static::Bool,
@@ -366,168 +338,6 @@ function get_constraints(
 end
 
 
-# Constraints: ScalarAffineFunction, LessThan
-# -------------------------------------------------------------------------
-function get_constraint_rhs(
-    constr::ConstraintRef{
-        Model,
-        MathOptInterface.ConstraintIndex{
-            MathOptInterface.ScalarAffineFunction{T},
-            MathOptInterface.LessThan{T},
-        },
-        ScalarShape,
-    },
-)::T where T
-    return MOI.get(
-        constr.model.moi_backend,
-        MOI.ConstraintSet(),
-        constr.index,
-    ).upper
-end
-
-
-function _terms_dict(constr)
-    terms = MOI.get(
-        constr.model.moi_backend,
-        MOI.ConstraintFunction(),
-        constr.index,
-    ).terms
-    return Dict(
-        MOI.get(
-            constr.model.moi_backend,
-            MOI.VariableName(),
-            term.variable_index
-        ) => term.coefficient
-        for term in terms
-    )
-end
-
-
-function get_constraint_lhs(
-    constr::ConstraintRef{
-        Model,
-        MathOptInterface.ConstraintIndex{
-            MathOptInterface.ScalarAffineFunction{T},
-            MathOptInterface.LessThan{T},
-        },
-        ScalarShape,
-    },
-)::Dict{String, T} where T
-    return _terms_dict(constr)
-end
-
-
-function get_constraint_sense(
-    constr::ConstraintRef{
-        Model,
-        MathOptInterface.ConstraintIndex{
-            MathOptInterface.ScalarAffineFunction{T},
-            MathOptInterface.LessThan{T},
-        },
-        ScalarShape,
-    },
-)::String where T
-    return "<"
-end
-
-
-# Constraints: ScalarAffineFunction, GreaterThan
-# -------------------------------------------------------------------------
-function get_constraint_rhs(
-    constr::ConstraintRef{
-        Model,
-        MathOptInterface.ConstraintIndex{
-            MathOptInterface.ScalarAffineFunction{T},
-            MathOptInterface.GreaterThan{T},
-        },
-        ScalarShape,
-    },
-)::T where T
-    return MOI.get(
-        constr.model.moi_backend,
-        MOI.ConstraintSet(),
-        constr.index,
-    ).lower
-end
-
-
-function get_constraint_lhs(
-    constr::ConstraintRef{
-        Model,
-        MathOptInterface.ConstraintIndex{
-            MathOptInterface.ScalarAffineFunction{T},
-            MathOptInterface.GreaterThan{T},
-        },
-        ScalarShape,
-    },
-)::Dict{String, T} where T
-    return _terms_dict(constr)
-end
-
-
-function get_constraint_sense(
-    constr::ConstraintRef{
-        Model,
-        MathOptInterface.ConstraintIndex{
-            MathOptInterface.ScalarAffineFunction{T},
-            MathOptInterface.GreaterThan{T},
-        },
-        ScalarShape,
-    },
-)::String where T
-    return ">"
-end
-
-
-# Constraints: ScalarAffineFunction, EqualTo
-# -------------------------------------------------------------------------
-function get_constraint_rhs(
-    constr::ConstraintRef{
-        Model,
-        MathOptInterface.ConstraintIndex{
-            MathOptInterface.ScalarAffineFunction{T},
-            MathOptInterface.EqualTo{T},
-        },
-        ScalarShape,
-    },
-)::T where T
-    return MOI.get(
-        constr.model.moi_backend,
-        MOI.ConstraintSet(),
-        constr.index,
-    ).value
-end
-
-
-function get_constraint_lhs(
-    constr::ConstraintRef{
-        Model,
-        MathOptInterface.ConstraintIndex{
-            MathOptInterface.ScalarAffineFunction{T},
-            MathOptInterface.EqualTo{T},
-        },
-        ScalarShape,
-    },
-)::Dict{String, T} where T
-    return _terms_dict(constr)
-end
-
-
-function get_constraint_sense(
-    constr::ConstraintRef{
-        Model,
-        MathOptInterface.ConstraintIndex{
-            MathOptInterface.ScalarAffineFunction{T},
-            MathOptInterface.EqualTo{T},
-        },
-        ScalarShape,
-    },
-)::String where T
-    return "="
-end
-
-# Test instances
-# ---------------------------------------------
 function build_test_instance_knapsack()
     weights = [23.0, 26.0, 20.0, 18.0]
     prices = [505.0, 352.0, 458.0, 220.0]
@@ -559,68 +369,55 @@ end
         ) 
     end
 
-    set_warm_start(self, solution) =
-        set_warm_start!(self.data, solution)
+    add_constraints(self, cf) =
+        error("not implemented")
+
+    are_constraints_satisfied(self, cf; tol=1e-5) =
+        error("not implemented")
+
+    build_test_instance_infeasible(self) =
+        error("not implemented")
+
+    build_test_instance_knapsack(self) =
+        build_test_instance_knapsack()
+    
+    # FIXME: Actually clone instead of returning self
+    clone(self) = self
 
     fix(self, solution) =
         fix!(self.data, solution)
     
-    set_instance(self, instance, model) =
-        set_instance!(self.data, instance, model)
-    
-    solve(
-        self;
-        tee=false,
-        iteration_cb=nothing,
-        lazy_cb=nothing,
-        user_cut_cb=nothing,
-    ) = solve(
-        self.data,
-        tee=tee,
-        iteration_cb=iteration_cb,
-    )
-    
-    solve_lp(self; tee=false) =
-        solve_lp(self.data, tee=tee)
-    
     get_solution(self) =
         self.data.solution
 
+    get_constraints(
+        self;
+        with_static=true,
+        with_sa=true,
+        with_lhs=true,
+    ) = get_constraints(
+        self.data,
+        with_static=with_static,
+    )
+
+    get_constraint_attrs(self) = [
+        # "basis_status",
+        "categories",
+        "dual_values",
+        "lazy",
+        "lhs",
+        "names",
+        "rhs",
+        # "sa_rhs_down",
+        # "sa_rhs_up",
+        "senses",
+        # "slacks",
+        "user_features",
+    ]
+    
     get_variables(self; with_static=true) =
         get_variables(self.data; with_static=with_static)
     
-    set_branching_priorities(self, priorities) =
-        @warn "JuMPSolver: set_branching_priorities not implemented"
-    
-    add_constraint(self, constraint) =
-        nothing
-
-    get_variable_names(self) =
-        get_variable_names(self.data)
-
-    is_infeasible(self) =
-        is_infeasible(self.data)
-
-    get_constraints(self; with_static=true) =
-        get_constraints(self.data; with_static=with_static)
-
-    get_constraint_ids(self) =
-        get_constraint_ids(self.data)
-
-    get_constraint_rhs(self, cname) =
-        get_constraint_rhs(self.data, cname)
-
-    get_constraint_lhs(self, cname) =
-        get_constraint_lhs(self.data, cname)
-
-    get_constraint_sense(self, cname) =
-        get_constraint_sense(self.data, cname)
-
-    build_test_instance_knapsack(self) =
-        build_test_instance_knapsack()
-
-    clone(self) = self
-
     get_variable_attrs(self) =  [
         "names",
         # "basis_status",
@@ -640,34 +437,33 @@ end
         "values",
     ]
 
-    get_constraint_attrs(self) = [
-        # "basis_status",
-        "categories",
-        "dual_values",
-        "lazy",
-        "lhs",
-        "names",
-        "rhs",
-        # "sa_rhs_down",
-        # "sa_rhs_up",
-        "senses",
-        # "slacks",
-        "user_features",
-    ]
+    is_infeasible(self) =
+        is_infeasible(self.data)
 
-    add_cut(self) = error("not implemented")
-    extract_constraint(self) = error("not implemented")
-    is_constraint_satisfied(self) = error("not implemented")
-    set_constraint_sense(self) = error("not implemented")
-    relax(self) = error("not implemented")
-    get_inequality_slacks(self) = error("not implemented")
-    get_dual(self) = error("not implemented")
-    get_sense(self) = error("not implemented")
-    build_test_instance_infeasible(self) = error("not implemented")
-    build_test_instance_redundancy(self) = error("not implemented")
-    get_constraints_old(self) = error("not implemented")
-    is_constraint_satisfied_old(self) = error("not implemented")
-    remove_constraint(self) = error("not implemented")
+    remove_constraints(self, names) =
+        error("not implemented")
+
+    set_instance(self, instance, model) =
+        set_instance!(self.data, instance, model)
+    
+    set_warm_start(self, solution) =
+        set_warm_start!(self.data, solution)
+
+    solve(
+        self;
+        tee=false,
+        iteration_cb=nothing,
+        lazy_cb=nothing,
+        user_cut_cb=nothing,
+    ) = solve(
+        self.data,
+        tee=tee,
+        iteration_cb=iteration_cb,
+    )
+    
+    solve_lp(self; tee=false) =
+        solve_lp(self.data, tee=tee)
 end
 
+
 export JuMPSolver, solve!, fit!, add!

src/problems/knapsack.jl

@@ -1,25 +0,0 @@
-#  MIPLearn: Extensible Framework for Learning-Enhanced Mixed-Integer Optimization
-#  Copyright (C) 2020-2021, UChicago Argonne, LLC. All rights reserved.
-#  Released under the modified BSD license. See COPYING.md for more details.
-
-using JuMP
-
-function knapsack_model(
-    weights::Array{Float64, 1},
-    prices::Array{Float64, 1},
-    capacity::Float64,
-)
-    model = Model()
-    n = length(weights)
-    @variable(model, x[0:(n-1)], Bin)
-    @objective(model, Max, sum(x[i] * prices[i+1] for i in 0:(n-1)))
-    @constraint(
-        model,
-        eq_capacity,
-        sum(
-            x[i] * weights[i+1]
-            for i in 0:(n-1)
-        ) <= capacity,
-    )
-    return model
-end