Reformat source code

src/Cuts/tableau/gmi.jl

@@ -9,7 +9,13 @@ using SparseArrays
 using Statistics
 using TimerOutputs
 
-function collect_gmi(mps_filename; optimizer, max_rounds=10, max_cuts_per_round=100, atol=1e-4)
+function collect_gmi(
+    mps_filename;
+    optimizer,
+    max_rounds = 10,
+    max_cuts_per_round = 100,
+    atol = 1e-4,
+)
     @info mps_filename
     reset_timer!()
 
@@ -98,12 +104,12 @@ function collect_gmi(mps_filename; optimizer, max_rounds=10, max_cuts_per_round=
         sol_frac = get_x(model_s)
         stats_time_select += @elapsed begin
             selected_rows =
-                select_gmi_rows(data_s, basis, sol_frac, max_rows=max_cuts_per_round)
+                select_gmi_rows(data_s, basis, sol_frac, max_rows = max_cuts_per_round)
         end
 
         # Compute selected tableau rows
         stats_time_tableau += @elapsed begin
-            tableau = compute_tableau(data_s, basis, sol_frac, rows=selected_rows)
+            tableau = compute_tableau(data_s, basis, sol_frac, rows = selected_rows)
 
             # Assert tableau rows have been computed correctly
             assert_eq(tableau.lhs * sol_frac, tableau.rhs)
@@ -180,10 +186,9 @@ function collect_gmi(mps_filename; optimizer, max_rounds=10, max_cuts_per_round=
     )
 end
 
-function select_gmi_rows(data, basis, x; max_rows=10, atol=1e-4)
+function select_gmi_rows(data, basis, x; max_rows = 10, atol = 1e-4)
     candidate_rows = [
-        r for
-        r in 1:length(basis.var_basic) if (
+        r for r = 1:length(basis.var_basic) if (
             (data.var_types[basis.var_basic[r]] != 'C') &&
             (frac(x[basis.var_basic[r]]) > atol) &&
             (frac2(x[basis.var_basic[r]]) > atol)
@@ -204,7 +209,7 @@ function compute_gmi(data::ProblemData, tableau::Tableau)::ConstraintSet
     lhs_J = Int[]
     lhs_V = Float64[]
     @timeit "Compute coefficients" begin
-        for k in 1:nnz(tableau.lhs)
+        for k = 1:nnz(tableau.lhs)
             i::Int = tableau_I[k]
             j::Int = tableau_J[k]
             v::Float64 = 0.0
@@ -235,4 +240,5 @@ function compute_gmi(data::ProblemData, tableau::Tableau)::ConstraintSet
     return ConstraintSet(; lhs, ub, lb)
 end
 
-export compute_gmi, frac, select_gmi_rows, assert_cuts_off, assert_does_not_cut_off, collect_gmi
+export compute_gmi,
+    frac, select_gmi_rows, assert_cuts_off, assert_does_not_cut_off, collect_gmi

src/Cuts/tableau/numerics.jl

@@ -2,7 +2,7 @@
 
 @inline frac2(x::Float64) = ceil(x) - x
 
-function assert_leq(a, b; atol=0.01)
+function assert_leq(a, b; atol = 0.01)
     if !all(a .<= b .+ atol)
         delta = a .- b
         for i in eachindex(delta)
@@ -14,7 +14,7 @@ function assert_leq(a, b; atol=0.01)
     end
 end
 
-function assert_eq(a, b; atol=1e-4)
+function assert_eq(a, b; atol = 1e-4)
     if !all(abs.(a .- b) .<= atol)
         delta = abs.(a .- b)
         for i in eachindex(delta)
@@ -26,7 +26,7 @@ function assert_eq(a, b; atol=1e-4)
     end
 end
 
-function assert_cuts_off(cuts::ConstraintSet, x::Vector{Float64}, tol=1e-6)
+function assert_cuts_off(cuts::ConstraintSet, x::Vector{Float64}, tol = 1e-6)
     for i = 1:length(cuts.lb)
         val = cuts.lhs[i, :]' * x
         if (val <= cuts.ub[i] - tol) && (val >= cuts.lb[i] + tol)
@@ -35,7 +35,7 @@ function assert_cuts_off(cuts::ConstraintSet, x::Vector{Float64}, tol=1e-6)
     end
 end
 
-function assert_does_not_cut_off(cuts::ConstraintSet, x::Vector{Float64}; tol=1e-6)
+function assert_does_not_cut_off(cuts::ConstraintSet, x::Vector{Float64}; tol = 1e-6)
     for i = 1:length(cuts.lb)
         val = cuts.lhs[i, :]' * x
         ub = cuts.ub[i]

src/components.jl

@@ -53,8 +53,14 @@ function __init_components__()
     )
     copy!(SelectTopSolutions, pyimport("miplearn.components.primal.mem").SelectTopSolutions)
     copy!(MergeTopSolutions, pyimport("miplearn.components.primal.mem").MergeTopSolutions)
-    copy!(MemorizingCutsComponent, pyimport("miplearn.components.cuts.mem").MemorizingCutsComponent)
-    copy!(MemorizingLazyComponent, pyimport("miplearn.components.lazy.mem").MemorizingLazyComponent)
+    copy!(
+        MemorizingCutsComponent,
+        pyimport("miplearn.components.cuts.mem").MemorizingCutsComponent,
+    )
+    copy!(
+        MemorizingLazyComponent,
+        pyimport("miplearn.components.lazy.mem").MemorizingLazyComponent,
+    )
 end
 
 export MinProbabilityClassifier,

src/io.jl

@@ -39,14 +39,14 @@ end
 function PyObject(m::SparseMatrixCSC)
     pyimport("scipy.sparse").csc_matrix(
         (m.nzval, m.rowval .- 1, m.colptr .- 1),
-        shape=size(m),
+        shape = size(m),
     ).tocoo()
 end
 
 function write_jld2(
     objs::Vector,
     dirname::AbstractString;
-    prefix::AbstractString=""
+    prefix::AbstractString = "",
 )::Vector{String}
     mkpath(dirname)
     filenames = [@sprintf("%s/%s%05d.jld2", dirname, prefix, i) for i = 1:length(objs)]

src/problems/setcover.jl

@@ -13,7 +13,7 @@ function __init_problems_setcover__()
     copy!(SetCoverGenerator, pyimport("miplearn.problems.setcover").SetCoverGenerator)
 end
 
-function build_setcover_model_jump(data::Any; optimizer=HiGHS.Optimizer)
+function build_setcover_model_jump(data::Any; optimizer = HiGHS.Optimizer)
     if data isa String
         data = read_pkl_gz(data)
     end

src/problems/stab.jl

@@ -10,10 +10,13 @@ global MaxWeightStableSetGenerator = PyNULL()
 
 function __init_problems_stab__()
     copy!(MaxWeightStableSetData, pyimport("miplearn.problems.stab").MaxWeightStableSetData)
-    copy!(MaxWeightStableSetGenerator, pyimport("miplearn.problems.stab").MaxWeightStableSetGenerator)
+    copy!(
+        MaxWeightStableSetGenerator,
+        pyimport("miplearn.problems.stab").MaxWeightStableSetGenerator,
+    )
 end
 
-function build_stab_model_jump(data::Any; optimizer=HiGHS.Optimizer)
+function build_stab_model_jump(data::Any; optimizer = HiGHS.Optimizer)
     nx = pyimport("networkx")
 
     if data isa String
@@ -50,11 +53,7 @@ function build_stab_model_jump(data::Any; optimizer=HiGHS.Optimizer)
         end
     end
 
-    return JumpModel(
-        model,
-        cuts_separate=cuts_separate,
-        cuts_enforce=cuts_enforce,
-    )
+    return JumpModel(model, cuts_separate = cuts_separate, cuts_enforce = cuts_enforce)
 end
 
 export MaxWeightStableSetData, MaxWeightStableSetGenerator, build_stab_model_jump

src/problems/tsp.jl

@@ -9,7 +9,10 @@ global TravelingSalesmanGenerator = PyNULL()
 
 function __init_problems_tsp__()
     copy!(TravelingSalesmanData, pyimport("miplearn.problems.tsp").TravelingSalesmanData)
-    copy!(TravelingSalesmanGenerator, pyimport("miplearn.problems.tsp").TravelingSalesmanGenerator)
+    copy!(
+        TravelingSalesmanGenerator,
+        pyimport("miplearn.problems.tsp").TravelingSalesmanGenerator,
+    )
 end
 
 function build_tsp_model_jump(data::Any; optimizer)
@@ -19,17 +22,15 @@ function build_tsp_model_jump(data::Any; optimizer)
         data = read_pkl_gz(data)
     end
     model = Model(optimizer)
-    edges = [(i, j) for i in 1:data.n_cities for j in (i+1):data.n_cities]
+    edges = [(i, j) for i = 1:data.n_cities for j = (i+1):data.n_cities]
     x = @variable(model, x[edges], Bin)
-    @objective(model, Min, sum(
-        x[(i, j)] * data.distances[i, j] for (i, j) in edges
-    ))
+    @objective(model, Min, sum(x[(i, j)] * data.distances[i, j] for (i, j) in edges))
 
     # Eq: Must choose two edges adjacent to each node
     @constraint(
         model,
         eq_degree[i in 1:data.n_cities],
-        sum(x[(min(i, j), max(i, j))] for j in 1:data.n_cities if i != j) == 2
+        sum(x[(min(i, j), max(i, j))] for j = 1:data.n_cities if i != j) == 2
     )
 
     function lazy_separate(cb_data)
@@ -41,10 +42,8 @@ function build_tsp_model_jump(data::Any; optimizer)
         for component in nx.connected_components(graph)
             if length(component) < data.n_cities
                 cut_edges = [
-                    [e[1], e[2]]
-                    for e in edges
-                    if (e[1] ∈ component && e[2] ∉ component)
-                    ||
+                    [e[1], e[2]] for
+                    e in edges if (e[1] ∈ component && e[2] ∉ component) ||
                     (e[1] ∉ component && e[2] ∈ component)
                 ]
                 push!(violations, cut_edges)
@@ -63,9 +62,9 @@ function build_tsp_model_jump(data::Any; optimizer)
 
     return JumpModel(
         model,
-        lazy_enforce=lazy_enforce,
-        lazy_separate=lazy_separate,
-        lp_optimizer=optimizer,
+        lazy_enforce = lazy_enforce,
+        lazy_separate = lazy_separate,
+        lp_optimizer = optimizer,
     )
 end
 

src/solvers/jump.jl

@@ -18,7 +18,7 @@ Base.@kwdef mutable struct _JumpModelExtData
     cuts_separate::Union{Function,Nothing} = nothing
     lazy_enforce::Union{Function,Nothing} = nothing
     lazy_separate::Union{Function,Nothing} = nothing
-    lp_optimizer
+    lp_optimizer::Any
 end
 
 function JuMP.copy_extension_data(
@@ -26,9 +26,7 @@ function JuMP.copy_extension_data(
     new_model::AbstractModel,
     ::AbstractModel,
 )
-    new_model.ext[:miplearn] = _JumpModelExtData(
-        lp_optimizer=old_ext.lp_optimizer
-    )
+    new_model.ext[:miplearn] = _JumpModelExtData(lp_optimizer = old_ext.lp_optimizer)
 end
 
 # -----------------------------------------------------------------------------
@@ -297,7 +295,7 @@ end
 function _fix_variables(model::JuMP.Model, var_names, var_values, stats)
     vars = [variable_by_name(model, v) for v in var_names]
     for (i, var) in enumerate(vars)
-        fix(var, var_values[i], force=true)
+        fix(var, var_values[i], force = true)
     end
 end
 
@@ -392,19 +390,19 @@ function __init_solvers_jump__()
         function __init__(
             self,
             inner;
-            cuts_enforce::Union{Function,Nothing}=nothing,
-            cuts_separate::Union{Function,Nothing}=nothing,
-            lazy_enforce::Union{Function,Nothing}=nothing,
-            lazy_separate::Union{Function,Nothing}=nothing,
-            lp_optimizer=HiGHS.Optimizer,
+            cuts_enforce::Union{Function,Nothing} = nothing,
+            cuts_separate::Union{Function,Nothing} = nothing,
+            lazy_enforce::Union{Function,Nothing} = nothing,
+            lazy_separate::Union{Function,Nothing} = nothing,
+            lp_optimizer = HiGHS.Optimizer,
         )
             self.inner = inner
             self.inner.ext[:miplearn] = _JumpModelExtData(
-                cuts_enforce=cuts_enforce,
-                cuts_separate=cuts_separate,
-                lazy_enforce=lazy_enforce,
-                lazy_separate=lazy_separate,
-                lp_optimizer=lp_optimizer,
+                cuts_enforce = cuts_enforce,
+                cuts_separate = cuts_separate,
+                lazy_enforce = lazy_enforce,
+                lazy_separate = lazy_separate,
+                lp_optimizer = lp_optimizer,
             )
         end
 
@@ -414,7 +412,7 @@ function __init_solvers_jump__()
             constrs_lhs,
             constrs_sense,
             constrs_rhs,
-            stats=nothing,
+            stats = nothing,
         ) = _add_constrs(
             self.inner,
             from_str_array(var_names),
@@ -430,14 +428,14 @@ function __init_solvers_jump__()
 
         extract_after_mip(self, h5) = _extract_after_mip(self.inner, h5)
 
-        fix_variables(self, var_names, var_values, stats=nothing) =
+        fix_variables(self, var_names, var_values, stats = nothing) =
             _fix_variables(self.inner, from_str_array(var_names), var_values, stats)
 
         optimize(self) = _optimize(self.inner)
 
         relax(self) = Class(_relax(self.inner))
 
-        set_warm_starts(self, var_names, var_values, stats=nothing) =
+        set_warm_starts(self, var_names, var_values, stats = nothing) =
             _set_warm_starts(self.inner, from_str_array(var_names), var_values, stats)
 
         write(self, filename) = _write(self.inner, filename)

test/src/MIPLearnT.jl

@@ -43,8 +43,8 @@ function runtests()
 end
 
 function format()
-    JuliaFormatter.format(BASEDIR, verbose=true)
-    JuliaFormatter.format("$BASEDIR/../../src", verbose=true)
+    JuliaFormatter.format(BASEDIR, verbose = true)
+    JuliaFormatter.format("$BASEDIR/../../src", verbose = true)
     return
 end
 

test/src/components/test_cuts.jl

@@ -10,34 +10,32 @@ function gen_stab()
     randint = pyimport("scipy.stats").randint
     np.random.seed(42)
     gen = MaxWeightStableSetGenerator(
-        w=uniform(10.0, scale=1.0),
-        n=randint(low=50, high=51),
-        p=uniform(loc=0.5, scale=0.0),
-        fix_graph=true,
+        w = uniform(10.0, scale = 1.0),
+        n = randint(low = 50, high = 51),
+        p = uniform(loc = 0.5, scale = 0.0),
+        fix_graph = true,
     )
     data = gen.generate(1)
-    data_filenames = write_pkl_gz(data, "$BASEDIR/../fixtures", prefix="stab-n50-")
+    data_filenames = write_pkl_gz(data, "$BASEDIR/../fixtures", prefix = "stab-n50-")
     collector = BasicCollector()
     collector.collect(
         data_filenames,
-        data -> build_stab_model_jump(data, optimizer=SCIP.Optimizer),
-        progress=true,
-        verbose=true,
+        data -> build_stab_model_jump(data, optimizer = SCIP.Optimizer),
+        progress = true,
+        verbose = true,
     )
 end
 
 function test_cuts()
     data_filenames = ["$BASEDIR/../fixtures/stab-n50-00000.pkl.gz"]
     clf = pyimport("sklearn.dummy").DummyClassifier()
-    extractor = H5FieldsExtractor(
-        instance_fields=["static_var_obj_coeffs"],
-    )
-    comp = MemorizingCutsComponent(clf=clf, extractor=extractor)
-    solver = LearningSolver(components=[comp])
+    extractor = H5FieldsExtractor(instance_fields = ["static_var_obj_coeffs"])
+    comp = MemorizingCutsComponent(clf = clf, extractor = extractor)
+    solver = LearningSolver(components = [comp])
     solver.fit(data_filenames)
     stats = solver.optimize(
         data_filenames[1],
-        data -> build_stab_model_jump(data, optimizer=SCIP.Optimizer),
+        data -> build_stab_model_jump(data, optimizer = SCIP.Optimizer),
     )
     @test stats["Cuts: AOT"] > 0
 end

test/src/components/test_lazy.jl

@@ -11,36 +11,34 @@ function gen_tsp()
     np.random.seed(42)
 
     gen = TravelingSalesmanGenerator(
-        x=uniform(loc=0.0, scale=1000.0),
-        y=uniform(loc=0.0, scale=1000.0),
-        n=randint(low=20, high=21),
-        gamma=uniform(loc=1.0, scale=0.25),
-        fix_cities=true,
-        round=true,
+        x = uniform(loc = 0.0, scale = 1000.0),
+        y = uniform(loc = 0.0, scale = 1000.0),
+        n = randint(low = 20, high = 21),
+        gamma = uniform(loc = 1.0, scale = 0.25),
+        fix_cities = true,
+        round = true,
     )
     data = gen.generate(1)
-    data_filenames = write_pkl_gz(data, "$BASEDIR/../fixtures", prefix="tsp-n20-")
+    data_filenames = write_pkl_gz(data, "$BASEDIR/../fixtures", prefix = "tsp-n20-")
     collector = BasicCollector()
     collector.collect(
         data_filenames,
-        data -> build_tsp_model_jump(data, optimizer=GLPK.Optimizer),
-        progress=true,
-        verbose=true,
+        data -> build_tsp_model_jump(data, optimizer = GLPK.Optimizer),
+        progress = true,
+        verbose = true,
     )
 end
 
 function test_lazy()
     data_filenames = ["$BASEDIR/../fixtures/tsp-n20-00000.pkl.gz"]
     clf = pyimport("sklearn.dummy").DummyClassifier()
-    extractor = H5FieldsExtractor(
-        instance_fields=["static_var_obj_coeffs"],
-    )
-    comp = MemorizingLazyComponent(clf=clf, extractor=extractor)
-    solver = LearningSolver(components=[comp])
+    extractor = H5FieldsExtractor(instance_fields = ["static_var_obj_coeffs"])
+    comp = MemorizingLazyComponent(clf = clf, extractor = extractor)
+    solver = LearningSolver(components = [comp])
     solver.fit(data_filenames)
     stats = solver.optimize(
         data_filenames[1],
-        data -> build_tsp_model_jump(data, optimizer=GLPK.Optimizer),
+        data -> build_tsp_model_jump(data, optimizer = GLPK.Optimizer),
     )
     @test stats["Lazy Constraints: AOT"] > 0
 end

test/src/problems/test_stab.jl

@@ -8,11 +8,11 @@ using SCIP
 function test_problems_stab()
     nx = pyimport("networkx")
     data = MaxWeightStableSetData(
-        graph=nx.gnp_random_graph(25, 0.5, seed=42),
-        weights=repeat([1.0], 25),
+        graph = nx.gnp_random_graph(25, 0.5, seed = 42),
+        weights = repeat([1.0], 25),
     )
     h5 = H5File(tempname(), "w")
-    model = build_stab_model_jump(data, optimizer=SCIP.Optimizer)
+    model = build_stab_model_jump(data, optimizer = SCIP.Optimizer)
     model.extract_after_load(h5)
     model.optimize()
     model.extract_after_mip(h5)

test/src/problems/test_tsp.jl

@@ -10,17 +10,12 @@ function test_problems_tsp()
     squareform = pyimport("scipy.spatial.distance").squareform
 
     data = TravelingSalesmanData(
-        n_cities=6,
-        distances=squareform(pdist([
-            [0.0, 0.0],
-            [1.0, 0.0],
-            [2.0, 0.0],
-            [3.0, 0.0],
-            [0.0, 1.0],
-            [3.0, 1.0],
-        ])),
+        n_cities = 6,
+        distances = squareform(
+            pdist([[0.0, 0.0], [1.0, 0.0], [2.0, 0.0], [3.0, 0.0], [0.0, 1.0], [3.0, 1.0]]),
+        ),
     )
-    model = build_tsp_model_jump(data, optimizer=GLPK.Optimizer)
+    model = build_tsp_model_jump(data, optimizer = GLPK.Optimizer)
     model.optimize()
     @test objective_value(model.inner) == 8.0
     return

test/src/test_io.jl

@@ -46,7 +46,7 @@ function test_jld2()
         _TestStruct(2, [1.0, 2.0, 3.0]),
         _TestStruct(3, [3.0, 3.0, 3.0]),
     ]
-    filenames = write_jld2(data, dirname, prefix="obj")
+    filenames = write_jld2(data, dirname, prefix = "obj")
     @test all(
         filenames .==
         ["$dirname/obj00001.jld2", "$dirname/obj00002.jld2", "$dirname/obj00003.jld2"],

test/src/test_usage.jl

@@ -13,16 +13,16 @@ function test_usage()
 
     @debug "Setting up LearningSolver..."
     solver = LearningSolver(
-        components=[
+        components = [
             IndependentVarsPrimalComponent(
-                base_clf=SingleClassFix(
+                base_clf = SingleClassFix(
                     MinProbabilityClassifier(
-                        base_clf=LogisticRegression(),
-                        thresholds=[0.95, 0.95],
+                        base_clf = LogisticRegression(),
+                        thresholds = [0.95, 0.95],
                     ),
                 ),
-                extractor=AlvLouWeh2017Extractor(),
-                action=SetWarmStart(),
+                extractor = AlvLouWeh2017Extractor(),
+                action = SetWarmStart(),
             ),
         ],
     )