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!()
 
@@ -182,8 +188,7 @@ end
 
 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/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

@@ -46,7 +46,7 @@ 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/stab.jl

@@ -10,7 +10,10 @@ 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)
@@ -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)

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
 
 # -----------------------------------------------------------------------------

test/src/components/test_cuts.jl

@@ -29,9 +29,7 @@ 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"],
-    )
+    extractor = H5FieldsExtractor(instance_fields = ["static_var_obj_coeffs"])
     comp = MemorizingCutsComponent(clf = clf, extractor = extractor)
     solver = LearningSolver(components = [comp])
     solver.fit(data_filenames)

test/src/components/test_lazy.jl

@@ -32,9 +32,7 @@ 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"],
-    )
+    extractor = H5FieldsExtractor(instance_fields = ["static_var_obj_coeffs"])
     comp = MemorizingLazyComponent(clf = clf, extractor = extractor)
     solver = LearningSolver(components = [comp])
     solver.fit(data_filenames)

test/src/problems/test_tsp.jl

@@ -11,14 +11,9 @@ function test_problems_tsp()
 
     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],
-        ])),
+        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.optimize()