def test_sample_regret(players, strategies):
n = 100
game = gamegen.role_symmetric_game(players, strategies)
max_regret = np.max(game.max_payoffs() - game.min_payoffs())
mix = game.random_mixtures()[0]
dev_profs = game.random_deviator_profiles(mix, n)
dev_profs.shape = (-1, game.num_role_strats)
inds = np.broadcast_to(np.arange(game.num_role_strats),
(n, game.num_role_strats)).flat
dev_payoffs = np.fromiter(
(game.get_payoffs(p)[i] for p, i in zip(dev_profs, inds)),
float, n * game.num_role_strats)
dev_payoffs.shape = (n, game.num_role_strats)
mix_payoffs = game.role_reduce(dev_payoffs * mix)
boots = bootstrap.sample_regret(game, mix_payoffs, dev_payoffs, 200)
assert boots.shape == (200,)
assert np.all(boots <= max_regret)
assert np.all(boots >= 0)
perc_boots = bootstrap.sample_regret(game, mix_payoffs, dev_payoffs, 200,
[2.5, 97.5])
assert perc_boots.shape == (2,)
assert np.all(perc_boots <= max_regret)
assert np.all(perc_boots >= 0)
def main(args):
data = json.load(args.input)
if args.regret is not None:
game, serial = gameio.read_base_game(json.load(args.regret[0]))
mix = serial.from_prof_json(json.load(args.regret[1]))
devs = load_devs(game, serial, data)
expect = game.role_reduce(devs * mix)
result = bootstrap.sample_regret(game, expect, devs, args.num_bootstraps, args.percentiles)
if args.mean:
mdevs = devs.mean(0)
mexpect = game.role_reduce(mdevs * mix, keepdims=True)
mean = np.max(mdevs - mexpect)
elif args.dev_surplus is not None:
game, serial = gameio.read_base_game(json.load(args.dev_surplus[0]))
mix = serial.from_prof_json(json.load(args.dev_surplus[1]))
devs = load_devs(game, serial, data)
surpluses = np.sum(devs * mix * game.role_repeat(game.num_players), 1)
result = bootstrap.mean(surpluses, args.num_bootstraps, args.percentiles)
if args.mean:
mean = surpluses.mean()
else:
data = np.asarray(data, float)
result = bootstrap.mean(data, args.num_bootstraps, args.percentiles)
if args.mean:
mean = data.mean()
if args.percentiles is None:
args.percentiles = np.linspace(0, 100, args.num_bootstraps)
jresult = {str(p).rstrip("0").rstrip("."): v.item() for p, v in zip(args.percentiles, result)}
if args.mean:
jresult["mean"] = mean
json.dump(jresult, args.output)
args.output.write("\n")
