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MIPLearn.jl
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DualGMI: Reimplement Expert using kNN component

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Alinson S. Xavier 2 months ago
parent a9f1b2c394
commit 5c522dbc5f
1 changed files with 20 additions and 142 deletions
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162
src/Cuts/tableau/gmi_dual.jl
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@ -8,6 +8,8 @@ using HiGHS
using Random
using DataStructures
import ..H5FieldsExtractor
global ExpertDualGmiComponent = PyNULL()
global KnnDualGmiComponent = PyNULL()
@ -253,138 +255,6 @@ function collect_gmi_dual(
)
end
function ExpertDualGmiComponent_before_mip(test_h5, model, _)
# Read cuts and optimal solution
h5 = H5File(test_h5, "r")
sol_opt_dict = Dict(
zip(
h5.get_array("static_var_names"),
convert(Array{Float64}, h5.get_array("mip_var_values")),
),
)
cut_basis_vars = h5.get_array("cuts_basis_vars")
cut_basis_sizes = h5.get_array("cuts_basis_sizes")
cut_rows = h5.get_array("cuts_rows")
obj_mip = h5.get_scalar("mip_lower_bound")
if obj_mip === nothing
obj_mip = h5.get_scalar("mip_obj_value")
end
h5.close()
# Initialize stats
stats_time_convert = 0
stats_time_tableau = 0
stats_time_gmi = 0
all_cuts = nothing
stats_time_convert = @elapsed begin
# Extract problem data
data = ProblemData(model)
# Construct optimal solution vector (with correct variable sequence)
sol_opt = [sol_opt_dict[n] for n in data.var_names]
# Assert optimal solution is feasible for the original problem
assert_leq(data.constr_lb, data.constr_lhs * sol_opt)
assert_leq(data.constr_lhs * sol_opt, data.constr_ub)
# Convert to standard form
data_s, transforms = convert_to_standard_form(data)
model_s = to_model(data_s)
set_optimizer(model_s, HiGHS.Optimizer)
relax_integrality(model_s)
# Convert optimal solution to standard form
sol_opt_s = forward(transforms, sol_opt)
# Assert converted solution is feasible for standard form problem
assert_eq(data_s.constr_lhs * sol_opt_s, data_s.constr_lb)
end
current_basis = nothing
for (r, row) in enumerate(cut_rows)
stats_time_tableau += @elapsed begin
if r == 1 || cut_basis_vars[r, :] != cut_basis_vars[r-1, :]
vbb, vnn, cbb, cnn = cut_basis_sizes[r, :]
current_basis = Basis(;
var_basic = cut_basis_vars[r, 1:vbb],
var_nonbasic = cut_basis_vars[r, vbb+1:vbb+vnn],
constr_basic = cut_basis_vars[r, vbb+vnn+1:vbb+vnn+cbb],
constr_nonbasic = cut_basis_vars[r, vbb+vnn+cbb+1:vbb+vnn+cbb+cnn],
)
end
tableau = compute_tableau(data_s, current_basis, rows = [row])
assert_eq(tableau.lhs * sol_opt_s, tableau.rhs)
end
stats_time_gmi += @elapsed begin
cuts_s = compute_gmi(data_s, tableau)
assert_does_not_cut_off(cuts_s, sol_opt_s)
end
cuts = backwards(transforms, cuts_s)
assert_does_not_cut_off(cuts, sol_opt)
if all_cuts === nothing
all_cuts = cuts
else
all_cuts.lhs = [all_cuts.lhs; cuts.lhs]
all_cuts.lb = [all_cuts.lb; cuts.lb]
all_cuts.ub = [all_cuts.ub; cuts.ub]
end
end
# Strategy 1: Add all cuts during the first call
function cut_callback_1(cb_data)
if all_cuts !== nothing
constrs = build_constraints(model, all_cuts)
@info "Enforcing $(length(constrs)) cuts..."
for c in constrs
MOI.submit(model, MOI.UserCut(cb_data), c)
end
all_cuts = nothing
end
end
# Strategy 2: Add violated cuts repeatedly until unable to separate
callback_disabled = false
function cut_callback_2(cb_data)
if callback_disabled
return
end
x = all_variables(model)
x_val = callback_value.(cb_data, x)
lhs_val = all_cuts.lhs * x_val
is_violated = lhs_val .> all_cuts.ub
selected_idx = findall(is_violated .== true)
selected_cuts = ConstraintSet(
lhs=all_cuts.lhs[selected_idx, :],
ub=all_cuts.ub[selected_idx],
lb=all_cuts.lb[selected_idx],
)
constrs = build_constraints(model, selected_cuts)
if length(constrs) > 0
@info "Enforcing $(length(constrs)) cuts..."
for c in constrs
MOI.submit(model, MOI.UserCut(cb_data), c)
end
else
@info "No violated cuts found. Disabling callback."
callback_disabled = true
end
end
# Set up cut callback
set_attribute(model, MOI.UserCutCallback(), cut_callback_1)
# set_attribute(model, MOI.UserCutCallback(), cut_callback_2)
stats = Dict()
stats["ExpertDualGmi: cuts"] = length(all_cuts.lb)
stats["ExpertDualGmi: time convert"] = stats_time_convert
stats["ExpertDualGmi: time tableau"] = stats_time_tableau
stats["ExpertDualGmi: time gmi"] = stats_time_gmi
return stats
end
function add_constraint_set_dual_v2(model::JuMP.Model, cs::ConstraintSet)
vars = all_variables(model)
nrows, ncols = size(cs.lhs)
@ -599,15 +469,7 @@ function KnnDualGmiComponent_before_mip(data::_KnnDualGmiData, test_h5, model, _
end
function __init_gmi_dual__()
@pydef mutable struct Class1
function fit(_, _) end
function before_mip(self, test_h5, model, stats)
ExpertDualGmiComponent_before_mip(test_h5, model.inner, stats)
end
end
copy!(ExpertDualGmiComponent, Class1)
@pydef mutable struct Class2
@pydef mutable struct KnnDualGmiComponentPy
function __init__(self; extractor, k = 3, strategy = "near")
self.data = _KnnDualGmiData(; extractor, k, strategy)
end
@ -618,7 +480,23 @@ function __init_gmi_dual__()
return @time KnnDualGmiComponent_before_mip(self.data, test_h5, model.inner, stats)
end
end
copy!(KnnDualGmiComponent, Class2)
copy!(KnnDualGmiComponent, KnnDualGmiComponentPy)
@pydef mutable struct ExpertDualGmiComponentPy
function __init__(self)
self.inner = KnnDualGmiComponentPy(
extractor=H5FieldsExtractor(instance_fields=["static_var_obj_coeffs"]),
k=1,
)
end
function fit(self, train_h5)
end
function before_mip(self, test_h5, model, stats)
self.inner.fit([test_h5])
return self.inner.before_mip(test_h5, model, stats)
end
end
copy!(ExpertDualGmiComponent, ExpertDualGmiComponentPy)
end
export collect_gmi_dual, expert_gmi_dual, ExpertDualGmiComponent, KnnDualGmiComponent

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