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analysis.py
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analysis.py
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import argparse
from functools import partial
from glob import glob
from pathlib import Path
from time import perf_counter
import matplotlib
import matplotlib.pyplot as plt
import numpy as np
from tqdm.contrib.concurrent import process_map
import hgspy
import plotting
import tools
from strategies.config import Config
from strategies.static import hgs
matplotlib.use("Agg") # Don't show plots
_SOLS_DIR = "solutions/"
_STATS_DIR = "statistics/"
_FIGS_DIR = "figures/"
def parse_args():
parser = argparse.ArgumentParser()
parser.add_argument("--seed", type=int, default=1)
parser.add_argument("--num_procs", type=int, default=4)
parser.add_argument("--config_loc", default="configs/analysis.toml")
parser.add_argument(
"--instance_pattern", default="instances/ORTEC-VRPTW-ASYM-*.txt"
)
parser.add_argument("--results_dir", type=str)
parser.add_argument("--overwrite", action="store_true")
group = parser.add_mutually_exclusive_group(required=True)
group.add_argument("--max_runtime", type=float)
group.add_argument("--max_iterations", type=int)
group.add_argument("--phase", choices=["quali", "final"])
return parser.parse_args()
def solve(
loc: str,
seed: int,
config_loc: str,
results_dir: str,
max_runtime,
max_iterations,
phase,
**kwargs,
):
path = Path(loc)
instance = tools.read_vrplib(path)
start = perf_counter()
if phase is not None:
t_lim = tools.static_time_limit(tools.name2size(loc), phase)
stop = hgspy.stop.MaxRuntime(t_lim)
elif max_runtime is not None:
stop = hgspy.stop.MaxRuntime(max_runtime)
else:
stop = hgspy.stop.MaxIterations(max_iterations)
static_config = Config.from_file(config_loc).static()
res = hgs(
instance,
hgspy.Config(seed=seed, **static_config.solver_params()),
static_config.node_ops(),
static_config.route_ops(),
static_config.crossover_ops(),
stop,
)
best = res.get_best_found()
routes = [route for route in best.get_routes() if route]
cost = best.cost()
try:
actual_cost = tools.validate_static_solution(instance, routes)
is_ok = "Y"
assert np.isclose(actual_cost, cost), "Could not validate objective."
except AssertionError:
is_ok = "N"
finish = round(perf_counter() - start, 3)
# Only save results for runs with feasible solutions and if results_dir
# is a non-empty string
if is_ok == "Y" and results_dir is not None:
save_results(instance, res, results_dir, path.stem)
stats = res.get_statistics()
return (
path.stem,
is_ok,
int(cost),
res.get_iterations(),
finish,
len(stats.incumbents()),
)
def save_results(instance, res, results_dir, inst_name):
"""
Save the best solution, statistics and figures of results.
- Solutions are stored as ``<results_dir>/solutions/<inst_name>.txt``.
- Statistics are stored as ``<results_dir>/statistics/<inst_name>.csv``.
- Figures are stored as ``<results_dir>/figures/<inst_name>.png``.
"""
res_path = Path(results_dir)
def make_path(subdir, extension):
return (res_path / subdir / inst_name).with_suffix("." + extension)
# Save best solutions
best = res.get_best_found()
sol_path = str(make_path(_SOLS_DIR, "txt"))
best.export_cvrplib_format(sol_path, res.get_run_time())
# Save statistics
stats = res.get_statistics()
stats_path = str(make_path(_STATS_DIR, "csv"))
stats.to_csv(stats_path)
# Save plots
fig = plt.figure(figsize=(20, 12))
gs = fig.add_gridspec(3, 2, width_ratios=(2 / 5, 3 / 5))
plotting.plot_population(fig.add_subplot(gs[0, 0]), stats)
plotting.plot_objectives(fig.add_subplot(gs[1, 0]), stats)
plotting.plot_incumbents(fig.add_subplot(gs[2, 0]), stats)
routes = best.get_routes()
plotting.plot_instance(fig.add_subplot(gs[:, 1]), instance, routes)
plt.tight_layout()
plt.savefig(make_path(_FIGS_DIR, "png"))
plt.close(fig)
def main():
args = parse_args()
# Make directories to save results
if args.results_dir is not None:
res_dir = Path(args.results_dir)
res_dir.mkdir(exist_ok=args.overwrite)
(res_dir / _SOLS_DIR).mkdir(exist_ok=args.overwrite)
(res_dir / _STATS_DIR).mkdir(exist_ok=args.overwrite)
(res_dir / _FIGS_DIR).mkdir(exist_ok=args.overwrite)
func = partial(solve, **vars(args))
func_args = sorted(glob(args.instance_pattern), key=tools.name2size)
tqdm_kwargs = dict(max_workers=args.num_procs, unit="instance")
data = process_map(func, func_args, **tqdm_kwargs)
dtypes = [
("inst", "U37"),
("ok", "U1"),
("obj", int),
("iters", int),
("time", float),
("nb_improv", int),
]
data = np.array(data, dtype=dtypes)
headers = [
"Instance",
"OK",
"Objective",
"Iters. (#)",
"Time (s)",
"Improv. (#)",
]
table = tools.tabulate(headers, data)
print("\n", table, "\n", sep="")
obj_all = data["obj"]
obj_feas = data[data["ok"] == "Y"]["obj"]
print(f" Avg. objective: {obj_all.mean():.0f}", end=" ")
print(f"(w/o infeas: {obj_feas.mean():.0f})" if obj_feas.size > 0 else "")
print(f" Avg. iterations: {data['iters'].mean():.0f}")
print(f" Avg. run-time (s): {data['time'].mean():.2f}")
print(f"Avg. improving moves: {data['nb_improv'].mean():.1f}")
print(f" Total not OK: {np.count_nonzero(data['ok'] == 'N')}")
if __name__ == "__main__":
main()