def test_choose_move():
np.random.seed(0)
start_state = Connect4State()
state1 = Connect4State("""\
.......
.......
.......
...XX..
OXOXO..
XOXOXOO
""")
expected_display = """\
.......
.......
.......
..XXX..
OXOXO..
XOXOXOO
"""
player = MctsPlayer(start_state, iteration_count=200)
move = player.choose_move(state1)
state2 = state1.make_move(move)
display = state2.display()
assert display == expected_display
def test_choose_move():
np.random.seed(0)
game = Connect4Game()
start_board = game.create_board("""\
.......
.......
.......
...XX..
OXOXO..
XOXOXOO
""")
expected_display = """\
.......
.......
.......
..XXX..
OXOXO..
XOXOXOO
"""
player = MctsPlayer(game, mcts_iterations=[200])
move = player.choose_move(start_board)
board = game.make_move(start_board, move)
display = game.display(board)
assert expected_display == display
def test_annotate():
start_state = TicTacToeState()
player = MctsPlayer(start_state,
iteration_count=10,
heuristic=FirstChoiceHeuristic())
player.choose_move(start_state)
move_probabilities = player.get_move_probabilities(start_state)
best_move, best_probability, best_count, best_value = move_probabilities[0]
assert best_move == '1A'
assert best_probability == approx(0.999013)
assert best_count == 9
assert best_value == approx(2 / 9)
def test_play_two():
start_state = FirstPlayerWinsGame()
controller = PlayController(start_state=start_state,
players=[MctsPlayer(start_state),
MctsPlayer(start_state)])
expected_p1_wins = 2
expected_ties = 0
expected_p2_wins = 0
p1_wins, ties, p2_wins = controller.play(games=2)
assert expected_p1_wins == p1_wins
assert expected_ties == ties
assert expected_p2_wins == p2_wins
def test_play_one_tie():
start_state = NoPlayerWinsGame()
controller = PlayController(start_state=start_state,
players=[MctsPlayer(start_state),
MctsPlayer(start_state)])
expected_p1_wins = 0
expected_ties = 1
expected_p2_wins = 0
p1_wins, ties, p2_wins = controller.play()
assert expected_p1_wins == p1_wins
assert expected_ties == ties
assert expected_p2_wins == p2_wins
def on_start(self):
self.game_start_time = datetime.now()
self.display.update_board(self.display.start_state)
self.is_history_dirty = False
ui = self.ui
player_fields = [(ui.player1.currentData(), ui.searches1.value()),
(ui.player2.currentData(), ui.searches2.value())]
is_shuffled = ui.shuffle_players.isChecked()
settings = get_settings(self.start_state)
settings.setValue('shuffle_players', is_shuffled)
if is_shuffled:
shuffle(player_fields)
mcts_choices = {
self.start_state.players[0]: player_fields[0],
self.start_state.players[1]: player_fields[1]
}
self.display.mcts_players = [
MctsPlayer(self.start_state,
player_number,
iteration_count=searches,
process_count=self.cpu_count)
for player_number, (heuristic, searches) in mcts_choices.items()
if heuristic is not None
]
layout: QGridLayout = ui.display_page.layout()
layout.replaceWidget(ui.game_display, self.display)
ui.game_display.setVisible(False)
ui.game_display = self.display
self.display.setVisible(True)
self.display.show_coordinates = ui.action_coordinates.isChecked()
self.on_view_game()
def test_win_scores_one():
""" Expose bug where search continues after a game-ending position. """
state1 = TicTacToeState("""\
..X
XX.
OO.
""")
player = MctsPlayer(TicTacToeState(), state1.X_PLAYER, iteration_count=100)
move = player.choose_move(state1)
search_node1 = player.search_manager.current_node.parent
for child_node in search_node1.children:
if child_node.move == 8:
assert child_node.average_value == 1.0
assert move == 8
def test_choose_move_in_pool():
start_state = Connect4State()
state1 = Connect4State("""\
.......
.......
.......
...XX..
OXOXO..
XOXOXOO
""")
player = MctsPlayer(start_state, iteration_count=200, process_count=2)
valid_moves = start_state.get_valid_moves()
move = player.choose_move(state1)
# Can't rely on which move, because other process has separate random seed.
assert valid_moves[move]
def test_display(capsys):
game = SecondPlayerWinsGame()
heuristic = FirstChoiceHeuristic()
players = [MctsPlayer(game,
game.X_PLAYER,
iteration_count=10,
heuristic=heuristic),
MctsPlayer(game,
game.O_PLAYER,
iteration_count=20,
heuristic=heuristic)]
controller = PlayController(game, players)
expected_output = """\
AB
1 ..
AB
1 X.
AB
1 XO
AB
1 ..
AB
1 X.
AB
1 XO
('mcts', 'first choice', '10 iterations') - 1 wins,
('mcts', 'first choice', '20 iterations') - 1 wins,
0 ties
"""
controller.play(games=2, flip=True, display=True)
out, err = capsys.readouterr()
expected_lines = expected_output.splitlines()
lines = out.splitlines()
assert lines[:-3] == expected_lines[:-3]
for line, expected_line in zip(lines[-3:], expected_lines[-3:]):
assert line.startswith(expected_line)
def test_player_results():
player = MctsPlayer(FirstPlayerWinsGame(), iteration_count=100)
player_results = PlayerResults(player)
player_results.move_count = 100
player_results.total_time = 1246 # seconds
player_results.win_count = 1
summary = player_results.get_summary()
assert summary == "('mcts', 'playout', '100 iterations') - 1 wins, 12.5s/move"
def test_choose_moves_at_random():
""" Early moves are chosen from a weighted random population. """
np.random.seed(0)
game = TicTacToeGame()
start_board = game.create_board("""\
...
...
X..
""")
player = MctsPlayer(game,
mcts_iterations=[80],
heuristic=[EarlyChoiceHeuristic(game)])
moves = set()
for _ in range(10):
move = player.choose_move(start_board)
moves.add(move)
player.search_manager.reset()
assert 1 < len(moves)
def test_choose_moves_at_random():
""" Early moves are chosen from a weighted random population. """
np.random.seed(0)
start_state = TicTacToeState()
state1 = TicTacToeState("""\
...
...
X..
""")
player = MctsPlayer(start_state,
iteration_count=80,
heuristic=EarlyChoiceHeuristic())
moves = set()
for _ in range(10):
move = player.choose_move(state1)
moves.add(move)
player.search_manager.reset()
assert 1 < len(moves)
def test_choose_move_sets_current_node():
np.random.seed(0)
start_state = Connect4State()
state1 = Connect4State("""\
.......
.......
.......
.......
OXOXOXO
XOXOXOX
""")
player = MctsPlayer(start_state, iteration_count=20)
move1 = player.choose_move(state1)
current_node1 = player.search_manager.current_node
state2 = state1.make_move(move1)
move2 = player.choose_move(state2)
current_node2 = player.search_manager.current_node
state3 = state2.make_move(move2)
assert current_node1.game_state == state2
assert current_node2.game_state == state3
def test_choose_move_no_iterations():
np.random.seed(0)
game = Connect4Game()
start_board = game.create_board("""\
.......
.......
.......
...XX..
OXOXO..
XOXOXOO
""")
test_count = 400
expected_count = test_count/7
expected_low = expected_count * 0.9
expected_high = expected_count * 1.1
move_counts = Counter()
for _ in range(test_count):
player = MctsPlayer(game, mcts_iterations=[0])
move = player.choose_move(start_board)
move_counts[move] += 1
assert expected_low < move_counts[2] < expected_high
def test_choose_move_no_iterations():
np.random.seed(0)
start_state = Connect4State()
state1 = Connect4State("""\
.......
.......
.......
...XX..
OXOXO..
XOXOXOO
""")
test_count = 400
expected_count = test_count / 7
expected_low = expected_count * 0.9
expected_high = expected_count * 1.1
move_counts = Counter()
for _ in range(test_count):
player = MctsPlayer(start_state, iteration_count=0)
move = player.choose_move(state1)
move_counts[move] += 1
assert expected_low < move_counts[2] < expected_high
def test_piece_hover_enter_mcts(pixmap_differ: PixmapDiffer):
""" Don't display move options while MCTS player is thinking. """
size = 240
with pixmap_differ.create_painters(
size, size,
'tictactoe_piece_hover_enter_mcts') as (actual, expected):
draw_square_grid(expected)
display = TicTacToeDisplay()
player = MctsPlayer(display.start_state, TicTacToeState.X_PLAYER)
display.mcts_players = (player, )
display.resize(324, 264)
display.ui.game_display.grab()
display.on_hover_enter(display.spaces[0][1])
render_display(display, actual)
display.close()
assert display.move_text.text() == 'thinking'
expected_icon = display.player1_icon.toImage()
assert display.ui.player_pixmap.pixmap().toImage() == expected_icon