def test_benchmark():
# Generate training and test instances
train_instances = MaxWeightStableSetGenerator(
n=randint(low=25, high=26)).generate(5)
test_instances = MaxWeightStableSetGenerator(
n=randint(low=25, high=26)).generate(3)
# Training phase...
training_solver = LearningSolver()
training_solver.parallel_solve(train_instances, n_jobs=10)
# Test phase...
test_solvers = {
"Strategy A": LearningSolver(),
"Strategy B": LearningSolver(),
}
benchmark = BenchmarkRunner(test_solvers)
benchmark.fit(train_instances)
benchmark.parallel_solve(test_instances, n_jobs=2, n_trials=2)
assert benchmark.raw_results().values.shape == (12, 16)
benchmark.save_results("/tmp/benchmark.csv")
assert os.path.isfile("/tmp/benchmark.csv")
benchmark = BenchmarkRunner(test_solvers)
benchmark.load_results("/tmp/benchmark.csv")
assert benchmark.raw_results().values.shape == (12, 16)
def train():
problem_name, challenge_name = args["<challenge>"].split("/")
pkg = importlib.import_module("miplearn.problems.%s" % problem_name)
challenge = getattr(pkg, challenge_name)()
train_instances = challenge.training_instances
test_instances = challenge.test_instances
solver = LearningSolver(time_limit=time_limit,
solver=internal_solver,
components={})
solver.parallel_solve(train_instances, n_jobs=n_jobs)
save(train_instances, "%s/train_instances.bin" % basepath)
save(test_instances, "%s/test_instances.bin" % basepath)
def test_parallel_solve():
instances = [_get_instance() for _ in range(10)]
solver = LearningSolver()
results = solver.parallel_solve(instances, n_jobs=3)
assert len(results) == 10
for instance in instances:
assert len(instance.solution["x"].keys()) == 4