def analyse(in_files: str, out_file: str):
"""
Creates a summary file for the passed-in in-files, at the out-file
location. This file summarises all passed-in instances.
"""
file = Path(out_file)
if file.exists():
last_modified = file.lstat().st_mtime
timestamp = datetime.fromtimestamp(last_modified)
print(f"Printing {out_file} (last modified {timestamp})")
instances = pd.read_csv(out_file, skipfooter=1, engine="python")
else:
instances = pd.DataFrame()
for location in glob.iglob(in_files):
problem = Problem.from_file(location, delimiter=',')
sol = Solution.from_file(f"solutions/oracs_{problem.instance}.csv")
instance = dict([(func.__name__, func(sol))
for func in PARAMETERS + STATISTICS])
instances = instances.append(instance, ignore_index=True)
report = make_pivot_table(instances)
if not file.exists():
report.to_csv(out_file)
print(report)
def compute(parser, args):
measures = []
for instance in np.arange(1, 101):
location = Path(f"experiments/{args.experiment}/"
f"{instance}-{args.method}.json")
if not location.exists():
print(f"{parser.prog}: {location} does not exist; skipping.")
continue
Problem.from_instance(args.experiment, instance)
sol = Solution.from_file(location)
measures.append({name: fn(sol) for name, fn in MEASURES.items()})
return pd.DataFrame.from_records(measures, columns=MEASURES.keys())
def main():
if len(sys.argv) < 2:
raise ValueError(f"{sys.argv[0]}: expected file location.")
problem = Problem.from_file(sys.argv[1], delimiter=',')
solution = Solution.from_file(f"solutions/oracs_{problem.instance}.csv")
is_feasible = True
for idx, rule in enumerate(RULES):
result, message = rule(solution)
print(f"{idx}: {message}")
if not result:
is_feasible = False
exit(0 if is_feasible else 1)