Update benchmark scripts

master
Alinson S. Xavier 5 years ago
parent 203afc6993
commit a0062edb5a

@ -17,7 +17,6 @@ Options:
--train-time-limit=<n> Solver time limit during training in seconds [default: 3600]
--test-time-limit=<n> Solver time limit during test in seconds [default: 900]
--solver-threads=<n> Number of threads the solver is allowed to use [default: 4]
--solver=<s> Internal MILP solver to use [default: gurobi]
"""
import importlib
import logging
@ -38,6 +37,7 @@ import seaborn as sns
from miplearn import (
LearningSolver,
BenchmarkRunner,
GurobiPyomoSolver,
setup_logger,
)
@ -52,7 +52,6 @@ n_jobs = int(args["--jobs"])
n_threads = int(args["--solver-threads"])
train_time_limit = int(args["--train-time-limit"])
test_time_limit = int(args["--test-time-limit"])
internal_solver = args["--solver"]
def write_pickle_gz(obj, filename):
@ -86,9 +85,12 @@ def train():
if not os.path.isfile(done_filename):
train_instances = glob.glob(f"{basepath}/train/*.gz")
solver = LearningSolver(
time_limit=train_time_limit,
solver=internal_solver,
threads=n_threads,
solver=lambda: GurobiPyomoSolver(
params={
"TimeLimit": train_time_limit,
"Threads": n_threads,
}
),
)
solver.parallel_solve(train_instances, n_jobs=n_jobs)
Path(done_filename).touch(exist_ok=True)
@ -100,9 +102,12 @@ def test_baseline():
if not os.path.isfile(csv_filename):
solvers = {
"baseline": LearningSolver(
time_limit=test_time_limit,
solver=internal_solver,
threads=n_threads,
solver=lambda: GurobiPyomoSolver(
params={
"TimeLimit": train_time_limit,
"Threads": n_threads,
}
),
),
}
benchmark = BenchmarkRunner(solvers)
@ -117,14 +122,20 @@ def test_ml():
if not os.path.isfile(csv_filename):
solvers = {
"ml-exact": LearningSolver(
time_limit=test_time_limit,
solver=internal_solver,
threads=n_threads,
solver=lambda: GurobiPyomoSolver(
params={
"TimeLimit": train_time_limit,
"Threads": n_threads,
}
),
),
"ml-heuristic": LearningSolver(
time_limit=test_time_limit,
solver=internal_solver,
threads=n_threads,
solver=lambda: GurobiPyomoSolver(
params={
"TimeLimit": train_time_limit,
"Threads": n_threads,
}
),
mode="heuristic",
),
}

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