def test_get_liberties(self): gs = simple_board() pp = Preprocess(["liberties"]) feature = pp.state_to_tensor(gs)[0].transpose((1,2,0)) # todo - test liberties when > 8 one_hot_liberties = np.zeros((gs.size, gs.size, 8)) # black piece at (4,4) has a single liberty: (4,3) one_hot_liberties[4,4,0] = 1 # the black group in the top left corner has 2 liberties one_hot_liberties[0,0:3,1] = 1 # .. as do the white pieces on the left and right of the eye one_hot_liberties[3,4,1] = 1 one_hot_liberties[5,4,1] = 1 # the white group in the top left corner has 3 liberties one_hot_liberties[1,0:2,2] = 1 # ...as does the white piece at (4,5) one_hot_liberties[4,5,2] = 1 # ...and the black pieces on the sides of the eye one_hot_liberties[3,3,2] = 1 one_hot_liberties[5,3,2] = 1 # the black piece at (4,2) has 4 liberties one_hot_liberties[4,2,3] = 1 for i in range(8): self.assertTrue(np.all(feature[:,:,i] == one_hot_liberties[:,:,i]), "bad expectation: stones with %d liberties" % (i+1))
def test_get_self_atari_size(self): # TODO - at the moment there is no imminent self-atari for white gs = simple_board() pp = Preprocess(["self_atari_size"]) feature = pp.state_to_tensor(gs)[0].transpose((1,2,0)) self.assertTrue(np.all(feature == np.zeros((gs.size, gs.size, 8))))
def test_get_liberties(self):
gs = simple_board()
pp = Preprocess(["liberties"])
feature = pp.state_to_tensor(gs)[0].transpose((1, 2, 0))
# todo - test liberties when > 8
one_hot_liberties = np.zeros((gs.size, gs.size, 8))
# black piece at (4,4) has a single liberty: (4,3)
one_hot_liberties[4, 4, 0] = 1
# the black group in the top left corner has 2 liberties
one_hot_liberties[0, 0:3, 1] = 1
# .. as do the white pieces on the left and right of the eye
one_hot_liberties[3, 4, 1] = 1
one_hot_liberties[5, 4, 1] = 1
# the white group in the top left corner has 3 liberties
one_hot_liberties[1, 0:2, 2] = 1
# ...as does the white piece at (4,5)
one_hot_liberties[4, 5, 2] = 1
# ...and the black pieces on the sides of the eye
one_hot_liberties[3, 3, 2] = 1
one_hot_liberties[5, 3, 2] = 1
# the black piece at (4,2) has 4 liberties
one_hot_liberties[4, 2, 3] = 1
for i in range(8):
self.assertTrue(
np.all(feature[:, :, i] == one_hot_liberties[:, :, i]),
"bad expectation: stones with %d liberties" % (i + 1))
def test_get_board(self): gs = simple_board() pp = Preprocess(["board"]) feature = pp.state_to_tensor(gs)[0].transpose((1,2,0)) white_pos = np.asarray([ [0,0,0,0,0,0,0], [1,1,0,0,0,0,0], [0,0,0,0,0,0,0], [0,0,0,0,1,0,0], [0,0,0,0,0,1,0], [0,0,0,0,1,0,0], [0,0,0,0,0,0,0]]) black_pos = np.asarray([ [1,1,1,0,0,0,0], [0,0,0,0,0,0,0], [0,0,0,0,0,0,0], [0,0,0,1,0,0,0], [0,0,1,0,1,0,0], [0,0,0,1,0,0,0], [0,0,0,0,0,0,0]]) empty_pos = np.ones((gs.size, gs.size)) - (white_pos + black_pos) # check number of planes self.assertEqual(feature.shape, (gs.size, gs.size, 3)) # check return value against hand-coded expectation # (given that current_player is white) self.assertTrue(np.all(feature == np.dstack((white_pos, black_pos, empty_pos))))
def test_get_self_atari_size(self):
# TODO - at the moment there is no imminent self-atari for white
gs = simple_board()
pp = Preprocess(["self_atari_size"])
feature = pp.state_to_tensor(gs)[0].transpose((1, 2, 0))
self.assertTrue(np.all(feature == np.zeros((gs.size, gs.size, 8))))
def test_get_sensibleness(self): # TODO - there are no legal eyes at the moment gs = simple_board() pp = Preprocess(["sensibleness"]) feature = pp.state_to_tensor(gs)[0,0] # 1D tensor; no need to transpose expectation = np.zeros((gs.size, gs.size)) for (x,y) in gs.get_legal_moves(): if not (gs.is_eye((x,y), go.WHITE)): expectation[x,y] = 1 self.assertTrue(np.all(expectation == feature))
def test_get_sensibleness(self):
# TODO - there are no legal eyes at the moment
gs = simple_board()
pp = Preprocess(["sensibleness"])
feature = pp.state_to_tensor(gs)[0,
0] # 1D tensor; no need to transpose
expectation = np.zeros((gs.size, gs.size))
for (x, y) in gs.get_legal_moves():
if not (gs.is_eye((x, y), go.WHITE)):
expectation[x, y] = 1
self.assertTrue(np.all(expectation == feature))
def test_get_capture_size(self): # TODO - at the moment there is no imminent capture gs = simple_board() pp = Preprocess(["capture_size"]) feature = pp.state_to_tensor(gs)[0].transpose((1,2,0)) one_hot_capture = np.zeros((gs.size, gs.size, 8)) # there is no capture available; all legal moves are zero-capture for (x,y) in gs.get_legal_moves(): one_hot_capture[x,y,0] = 1 for i in range(8): self.assertTrue(np.all(feature[:,:,i] == one_hot_capture[:,:,i]), "bad expectation: capturing %d stones" % i)
def test_get_capture_size(self):
# TODO - at the moment there is no imminent capture
gs = simple_board()
pp = Preprocess(["capture_size"])
feature = pp.state_to_tensor(gs)[0].transpose((1, 2, 0))
one_hot_capture = np.zeros((gs.size, gs.size, 8))
# there is no capture available; all legal moves are zero-capture
for (x, y) in gs.get_legal_moves():
one_hot_capture[x, y, 0] = 1
for i in range(8):
self.assertTrue(
np.all(feature[:, :, i] == one_hot_capture[:, :, i]),
"bad expectation: capturing %d stones" % i)
def test_get_turns_since(self):
gs = simple_board()
pp = Preprocess(["turns_since"])
feature = pp.state_to_tensor(gs)[0].transpose((1, 2, 0))
one_hot_turns = np.zeros((gs.size, gs.size, 8))
rev_history = gs.history[::-1]
# one plane per move for the last 7
for i in range(7):
move = rev_history[i]
one_hot_turns[move[0], move[1], i] = 1
# far back plane gets all other moves
for move in rev_history[7:]:
one_hot_turns[move[0], move[1], 7] = 1
self.assertTrue(np.all(feature == one_hot_turns))
def test_get_turns_since(self): gs = simple_board() pp = Preprocess(["turns_since"]) feature = pp.state_to_tensor(gs)[0].transpose((1,2,0)) one_hot_turns = np.zeros((gs.size, gs.size, 8)) rev_history = gs.history[::-1] # one plane per move for the last 7 for i in range(7): move = rev_history[i] one_hot_turns[move[0], move[1], i] = 1 # far back plane gets all other moves for move in rev_history[7:]: one_hot_turns[move[0], move[1], 7] = 1 self.assertTrue(np.all(feature == one_hot_turns))
def convert_game(self, file_name):
with open(file_name, 'r') as file_object:
sgf_object = SGFParser(file_object.read())
c = sgf_object.parse().cursor()
tensors = []
actions = []
gs = go.GameState()
proc = Preprocess()
while True:
try:
move = self.parse_raw_move(c.next())
actions.append(self.encode_label(move))
gs.do_move(move)
tensors.append(proc.state_to_tensor(gs))
except GameTreeEndError:
# remove last board state since it has no label
tensors = tensors[0:-1]
break
return zip(tensors, actions)
def convert_game(self,file_name):
with open(file_name,'r') as file_object:
sgf_object = SGFParser(file_object.read())
c = sgf_object.parse().cursor()
tensors = []
actions = []
gs = go.GameState()
proc = Preprocess()
while True:
try:
move = self.parse_raw_move(c.next())
actions.append(self.encode_label(move))
gs.do_move(move)
tensors.append(proc.state_to_tensor(gs))
except GameTreeEndError:
# remove last board state since it has no label
tensors = tensors[0:-1]
break
return zip(tensors, actions)
def test_get_liberties_after(self): gs = simple_board() pp = Preprocess(["liberties_after"]) feature = pp.state_to_tensor(gs)[0].transpose((1,2,0)) one_hot_liberties = np.zeros((gs.size, gs.size, 8)) # TODO (?) hand-code? for (x,y) in gs.get_legal_moves(): copy = gs.copy() copy.do_move((x,y)) libs = copy.update_current_liberties()[x,y] if libs < 7: one_hot_liberties[x,y,libs] = 1 else: one_hot_liberties[x,y,7] = 1 for i in range(8): self.assertTrue(np.all(feature[:,:,i] == one_hot_liberties[:,:,i]), "bad expectation: stones with %d liberties after move" % (i+1))
def test_get_board(self):
gs = simple_board()
pp = Preprocess(["board"])
feature = pp.state_to_tensor(gs)[0].transpose((1, 2, 0))
white_pos = np.asarray([[0, 0, 0, 0, 0, 0, 0], [1, 1, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 1, 0, 0],
[0, 0, 0, 0, 0, 1, 0], [0, 0, 0, 0, 1, 0, 0],
[0, 0, 0, 0, 0, 0, 0]])
black_pos = np.asarray([[1, 1, 1, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 1, 0, 0, 0],
[0, 0, 1, 0, 1, 0, 0], [0, 0, 0, 1, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0]])
empty_pos = np.ones((gs.size, gs.size)) - (white_pos + black_pos)
# check number of planes
self.assertEqual(feature.shape, (gs.size, gs.size, 3))
# check return value against hand-coded expectation
# (given that current_player is white)
self.assertTrue(
np.all(feature == np.dstack((white_pos, black_pos, empty_pos))))
def test_get_liberties_after(self):
gs = simple_board()
pp = Preprocess(["liberties_after"])
feature = pp.state_to_tensor(gs)[0].transpose((1, 2, 0))
one_hot_liberties = np.zeros((gs.size, gs.size, 8))
# TODO (?) hand-code?
for (x, y) in gs.get_legal_moves():
copy = gs.copy()
copy.do_move((x, y))
libs = copy.liberty_counts[x, y]
if libs < 7:
one_hot_liberties[x, y, libs] = 1
else:
one_hot_liberties[x, y, 7] = 1
for i in range(8):
self.assertTrue(
np.all(feature[:, :, i] == one_hot_liberties[:, :, i]),
"bad expectation: stones with %d liberties after move" %
(i + 1))
def test_feature_concatenation(self):
gs = simple_board()
pp = Preprocess(["board", "sensibleness", "capture_size"])
feature = pp.state_to_tensor(gs)[0].transpose((1, 2, 0))
expectation = np.zeros((gs.size, gs.size, 3 + 1 + 8))
# first three planes: board
expectation[:, :, 0] = (gs.board == go.WHITE) * 1
expectation[:, :, 1] = (gs.board == go.BLACK) * 1
expectation[:, :, 2] = (gs.board == go.EMPTY) * 1
# 4th plane: sensibleness (as in test_get_sensibleness)
for (x, y) in gs.get_legal_moves():
if not (gs.is_eye((x, y), go.WHITE)):
expectation[x, y, 3] = 1
# 5th through 12th plane: capture size (all zero-capture)
for (x, y) in gs.get_legal_moves():
expectation[x, y, 4] = 1
self.assertTrue(np.all(expectation == feature))
def test_feature_concatenation(self): gs = simple_board() pp = Preprocess(["board", "sensibleness", "capture_size"]) feature = pp.state_to_tensor(gs)[0].transpose((1,2,0)) expectation = np.zeros((gs.size, gs.size, 3+1+8)) # first three planes: board expectation[:,:,0] = (gs.board == go.WHITE) * 1 expectation[:,:,1] = (gs.board == go.BLACK) * 1 expectation[:,:,2] = (gs.board == go.EMPTY) * 1 # 4th plane: sensibleness (as in test_get_sensibleness) for (x,y) in gs.get_legal_moves(): if not (gs.is_eye((x,y), go.WHITE)): expectation[x,y,3] = 1 # 5th through 12th plane: capture size (all zero-capture) for (x,y) in gs.get_legal_moves(): expectation[x,y,4] = 1 self.assertTrue(np.all(expectation == feature))