def test_ml():
logger.info("Loading instances...")
train_instances = load("%s/train_instances.bin" % basepath)
test_instances = load("%s/test_instances.bin" % basepath)
solvers = {
"ml-exact":
LearningSolver(
time_limit=time_limit,
solver=internal_solver,
),
"ml-heuristic":
LearningSolver(
time_limit=time_limit,
solver=internal_solver,
mode="heuristic",
),
}
benchmark = BenchmarkRunner(solvers)
logger.info("Loading results...")
benchmark.load_results("%s/benchmark_baseline.csv" % basepath)
logger.info("Fitting...")
benchmark.fit(train_instances)
logger.info("Solving...")
benchmark.parallel_solve(test_instances, n_jobs=n_jobs)
benchmark.save_results("%s/benchmark_ml.csv" % basepath)
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 test_baseline():
test_instances = load("%s/test_instances.bin" % basepath)
solvers = {
"baseline": LearningSolver(
time_limit=test_time_limit,
solver=internal_solver,
),
}
benchmark = BenchmarkRunner(solvers)
benchmark.parallel_solve(test_instances, n_jobs=n_jobs)
benchmark.save_results("%s/benchmark_baseline.csv" % basepath)