Skip to content

remove debugging code from test_nash.py #727

New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

Sign up for GitHub

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

Merged
tturocy merged 1 commit into from
Jan 5, 2026
Merged

remove debugging code from test_nash.py #727

Changes from all commits
Commits
Show all changes
1 commit
Select commit
File filter

Filter by extension

Filter by extension
Conversations
Failed to load comments.
Loading
Jump to
Jump to file
Failed to load files.
Loading
Diff view
Diff view
95 changes: 0 additions & 95 deletions tests/test_nash.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -208,14 +208,9 @@ def test_enumpoly_ordered_behavior(
result = gbt.nash.enumpoly_solve(game, use_strategic=False)
assert len(result.equilibria) == len(mixed_behav_prof_data)
for eq, exp in zip(result.equilibria, mixed_behav_prof_data, strict=True):
print("FOUND EQ:", eq)
print(eq.max_regret())
print(eq.agent_max_regret())
assert abs(eq.max_regret()) <= TOL
assert abs(eq.agent_max_regret()) <= TOL
expected = game.mixed_behavior_profile(rational=True, data=exp)
# print(expected)
# print(eq)
for p in game.players:
for i in p.infosets:
for a in i.actions:
Expand Down Expand Up @@ -273,14 +268,9 @@ def test_enumpoly_ordered_behavior_PROBLEM_CASE(
result = gbt.nash.enumpoly_solve(game, use_strategic=False)
assert len(result.equilibria) == len(mixed_behav_prof_data)
for eq, exp in zip(result.equilibria, mixed_behav_prof_data, strict=True):
print("FOUND EQ:", eq)
print("found max regret:", eq.max_regret())
print("found agent max regret:", eq.agent_max_regret())
assert abs(eq.max_regret()) <= TOL
assert abs(eq.agent_max_regret()) <= TOL
expected = game.mixed_behavior_profile(rational=True, data=exp)
print("exp max regret:", eq.max_regret())
print("exp agent max regret:", eq.agent_max_regret())
for p in game.players:
for i in p.infosets:
for a in i.actions:
Expand Down Expand Up @@ -876,88 +866,3 @@ def test_logit_solve_lambda():
game = games.read_from_file("const_sum_game.nfg")
assert len(gbt.qre.logit_solve_lambda(
game=game, lam=[1, 2, 3], first_step=0.2, max_accel=1)) > 0


def test_regrets_tmp():

prof_data_doub = []
prof_data_doub.append([[[1, 0], [1, 0]], [[1, 0], [0.5, 0.5]], [[1, 0], [0, 1]]])
# prof_data_doub.append([[[1, 0], [1, 0]], [[1, 0], [0, 1]], [[1, 0], [0.33333, 0.6666]]])
# prof_data_doub.append([[[1, 0], [1, 0]], [[1, 0], [0.5, 0.5]], [[0, 1], [1, 0]]])
# prof_data_doub.append([[[1, 0], [1, 0]], [[1, 0], [0, 1]], [[0.33333, 0.6666], [1, 0]]])

prof_data_rat = []
prof_data_rat.append([[[1, 0], [1, 0]], [[1, 0], ["1/2", "1/2"]], [[1, 0], [0, 1]]])
# prof_data_rat.append([[[1, 0], [1, 0]], [[1, 0], [0, 1]], [[1, 0], ["1/3", "2/3"]]])
# prof_data_rat.append([[[1, 0], [1, 0]], [[1, 0], ["1/2", "1/2"]], [[0, 1], [1, 0]]])
# prof_data_rat.append([[[1, 0], [1, 0]], [[1, 0], [0, 1]], [["1/3", "2/3"], [1, 0]]])

g = games.create_3_player_with_internal_outcomes_efg()

print()
print("==================")
for p in prof_data_doub:
prof = g.mixed_behavior_profile(rational=False, data=p)
print(prof.max_regret())
print(prof.agent_max_regret())
print("==================")
for p in prof_data_rat:
prof = g.mixed_behavior_profile(rational=True, data=p)
print(prof.max_regret())
print(prof.agent_max_regret())
print("==================")
for p in prof_data_doub:
prof = g.mixed_behavior_profile(rational=False, data=p)
print(prof.max_regret())
print(prof.agent_max_regret())


def test_regrets_tmp2():
g = games.create_3_player_with_internal_outcomes_efg()
prof_data_rat = [[[1, 0], [1, 0]], [[1, 0], ["1/2", "1/2"]], [[1, 0], [0, 1]]]
profile_rat = g.mixed_behavior_profile(rational=True, data=prof_data_rat)
print()
print(profile_rat.max_regret()) # 3/2
profile_rat = g.mixed_behavior_profile(rational=True, data=prof_data_rat)
print(profile_rat.max_regret()) # now different! 0


@pytest.mark.parametrize(
"game,mixed_behav_prof_data",
[
(
games.create_seq_form_STOC_paper_zero_sum_2_player_efg(),
[
[[0, 1], ["1/3", "2/3"], ["2/3", "1/3"]],
[["5/6", "1/6"], ["5/9", "4/9"]],
],
),
(
games.create_3_player_with_internal_outcomes_efg(),
[
[[1, 0], [1, 0]], [[1, 0], ["1/2", "1/2"]],
[[1, 0], [0, 1]]
],
),
(
games.create_STOC_simplified(),
[
[[0, 1], ["1/3", "2/3"], ["2/3", "1/3"]],
[["5/6", "1/6"]],
],
),
# (
# games.create_STOC_simplified2(),
# [
# [[1], [1], ["1/3", "2/3"]],
# [["5/6", "1/6"]],
# ],
# ),
],
)
def test_repeat_max_regret(game: gbt.Game, mixed_behav_prof_data: list):
profile1 = game.mixed_behavior_profile(rational=True, data=mixed_behav_prof_data)
mr1 = profile1.max_regret()
profile2 = game.mixed_behavior_profile(rational=True, data=mixed_behav_prof_data)
mr2 = profile2.max_regret()
assert mr1 == mr2