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.
72 lines
3.1 KiB
72 lines
3.1 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.problems.knapsack import MultiKnapsackGenerator, MultiKnapsackInstance
|
|
from scipy.stats import uniform, randint
|
|
import numpy as np
|
|
|
|
|
|
def test_knapsack_generator():
|
|
gen = MultiKnapsackGenerator(n=randint(low=100, high=101),
|
|
m=randint(low=30, high=31),
|
|
w=randint(low=0, high=1000),
|
|
K=randint(low=500, high=501),
|
|
u=uniform(loc=1.0, scale=1.0),
|
|
alpha=uniform(loc=0.50, scale=0.0),
|
|
)
|
|
instances = gen.generate(100)
|
|
w_sum = sum(instance.weights for instance in instances) / len(instances)
|
|
p_sum = sum(instance.prices for instance in instances) / len(instances)
|
|
b_sum = sum(instance.capacities for instance in instances) / len(instances)
|
|
assert round(np.mean(w_sum), -1) == 500.
|
|
assert round(np.mean(p_sum), -1) == 1250.
|
|
assert round(np.mean(b_sum), -3) == 25000.
|
|
|
|
|
|
def test_knapsack_instance():
|
|
instance = MultiKnapsackInstance(
|
|
prices=np.array([5.0, 10.0, 15.0]),
|
|
capacities=np.array([20.0, 30.0]),
|
|
weights=np.array([
|
|
[5.0, 5.0, 5.0],
|
|
[5.0, 10.0, 15.0],
|
|
])
|
|
)
|
|
|
|
assert (instance.get_instance_features() == np.array([
|
|
5.0, 10.0, 15.0, 20.0, 30.0, 5.0, 5.0, 5.0, 5.0, 10.0, 15.0
|
|
])).all()
|
|
|
|
solver = LearningSolver()
|
|
results = solver.solve(instance)
|
|
assert results["Problem"][0]["Lower bound"] == 30.0
|
|
|
|
def test_knapsack_fixed_weights_jitter():
|
|
gen = MultiKnapsackGenerator(n=randint(low=50, high=51),
|
|
m=randint(low=10, high=11),
|
|
w=randint(low=0, high=1000),
|
|
K=randint(low=500, high=501),
|
|
u=uniform(loc=1.0, scale=0.0),
|
|
alpha=uniform(loc=0.50, scale=0.0),
|
|
fix_w=True,
|
|
w_jitter=randint(low=0, high=1),
|
|
)
|
|
instances = gen.generate(100)
|
|
w = [instance.weights[0,0] for instance in instances]
|
|
assert np.std(w) == 0.
|
|
|
|
gen = MultiKnapsackGenerator(n=randint(low=1, high=2),
|
|
m=randint(low=10, high=11),
|
|
w=randint(low=1000, high=1001),
|
|
K=randint(low=500, high=501),
|
|
u=uniform(loc=1.0, scale=0.0),
|
|
alpha=uniform(loc=0.50, scale=0.0),
|
|
fix_w=True,
|
|
w_jitter=randint(low=0, high=1001),
|
|
)
|
|
instances = gen.generate(1_000)
|
|
w = [instance.weights[0,0] for instance in instances]
|
|
assert round(np.std(w), -1) == 290.
|
|
assert round(np.mean(w), -2) == 1500. |