You can not select more than 25 topics
Topics must start with a letter or number, can include dashes ('-') and can be up to 35 characters long.
45 lines
1.5 KiB
45 lines
1.5 KiB
# MIPLearn, an extensible framework for Learning-Enhanced Mixed-Integer Optimization
|
|
# Copyright (C) 2019-2020 Argonne National Laboratory. All rights reserved.
|
|
# Written by Alinson S. Xavier <axavier@anl.gov>
|
|
|
|
from miplearn import LearningSolver
|
|
from miplearn.transformers import PerVariableTransformer
|
|
from miplearn.problems.knapsack import MultiKnapsackInstance
|
|
import numpy as np
|
|
import pyomo.environ as pe
|
|
|
|
def test_transform_with_categories():
|
|
transformer = PerVariableTransformer()
|
|
instance = MultiKnapsackInstance(
|
|
weights=np.array([[23., 26., 20., 18.]]),
|
|
prices=np.array([505., 352., 458., 220.]),
|
|
capacities=np.array([67.])
|
|
)
|
|
model = instance.to_model()
|
|
solver = pe.SolverFactory('gurobi')
|
|
solver.options["threads"] = 1
|
|
solver.solve(model)
|
|
|
|
var_split = transformer.split_variables(instance, model)
|
|
var_split_expected = {
|
|
0: [(model.x, 0)],
|
|
1: [(model.x, 1)],
|
|
2: [(model.x, 2)],
|
|
3: [(model.x, 3)],
|
|
}
|
|
assert var_split == var_split_expected
|
|
|
|
var_index_pairs = var_split[0]
|
|
x_actual = transformer.transform_instance(instance, var_index_pairs)
|
|
x_expected = np.hstack([
|
|
instance.get_instance_features(),
|
|
instance.get_variable_features(model.x, 0),
|
|
])
|
|
assert (x_expected == x_actual).all()
|
|
|
|
solver.solve(model)
|
|
|
|
y_actual = transformer.transform_solution(var_index_pairs)
|
|
y_expected = np.array([[0., 1.]])
|
|
assert y_actual.tolist() == y_expected.tolist()
|