def evaluate_MCTS_against_winpct(data):
ally_wins = 0
enemy_wins = 0
total_win_pct = 0
ally_starting = data[0]
exploration_term = data[1]
state = MCTS.State()
state.ally_starting = ally_starting
tree = None
banned_champs = set(random.sample(range(0, 141), 10))
allowed_champions = MCTS.get_allowed_champions(banned_champs)
while len(state.enemy_team) < 5 or len(state.ally_team) < 5:
if ally_starting is not True:
pick_champ_enemy_team_winpct(allowed_champions, state)
tree = MCTS.recall_subtree(state, tree, set(banned_champs))
allowed_champions = list.copy(tree.possible_actions)
suggestions, tree = MCTS.run_mcts(10,
tree,
True,
allowed_champions,
exploration_term=exploration_term)
if suggestions[0].champ2 is None:
state.ally_team.append(suggestions[0].champ)
allowed_champions.remove(suggestions[0].champ)
else:
state.ally_team.append(suggestions[0].champ)
allowed_champions.remove(suggestions[0].champ)
state.ally_team.append(suggestions[0].champ2)
allowed_champions.remove(suggestions[0].champ2)
if ally_starting is True:
pick_champ_enemy_team_winpct(allowed_champions, state)
input_vector = list.copy(state.ally_team)
input_vector.extend(list.copy(state.enemy_team))
result = NN.predictTeamComp(input_vector)
total_win_pct += result
if result > 0.5:
ally_wins += 1
elif result < 0.5:
enemy_wins += 1
return ally_wins, enemy_wins, result
def test_get_allowed_champions2(test_input, already_chosen, expected):
assert mcts.get_allowed_champions(test_input, already_chosen) == expected
def test_get_allowed_champions(test_input, expected):
assert mcts.get_allowed_champions(test_input) == expected