def decode_ant_vision_2d_examples(encoded_examples: Tuple[ndarray, ndarray]) -> List[AntVision2DExample]:
gst = GameStateTranslator()
features, labels = encoded_examples
feature_example_count = features.shape[0]
feature_row_min = 0 - floor(features.shape[1] / 2)
feature_row_max = 0 + floor(features.shape[1] / 2)
feature_col_min = 0 - floor(features.shape[2] / 2)
feature_col_max = 0 + floor(features.shape[2] / 2)
row_nums = seq(range(feature_row_min, feature_row_max)).to_list()
col_nums = seq(range(feature_col_min, feature_col_max)).to_list()
if features.shape[3] != 7: raise ValueError(
'Only implemented for 7 channel encoding since down_sampling eliminates information')
items = []
for ex_index in range(feature_example_count):
example_features: Dict[Position, PositionState] = {}
for row_index, row_num in enumerate(row_nums):
for col_index, col_num in enumerate(col_nums):
position = Position(row_num, col_num)
enum_val = gst.convert_array_to_enum(features[ex_index, row_index, col_index].tolist(), PositionState)
example_features[position] = enum_val
direction = gst.convert_array_to_enum(labels[ex_index].tolist(), Direction)
items.append(AntVision2DExample(example_features, direction))
return items
def test_create_nn_input(self):
game_state = create_test_game_state()
test_ant = game_state.game_turns[0].ants.get(Position(1, 19))
ant_vision = game_state.game_map.get_positions_within_distance(
test_ant.position, game_state.view_radius_squared)
translator = GameStateTranslator()
nn_input = translator.convert_to_1d_example(test_ant, game_state)
self.assertIsNotNone(nn_input)
self.assertEqual((len(ant_vision) - 1), len(nn_input.features))
def test_convert_enums_to_array(self):
translator = GameStateTranslator()
array = translator.convert_enum_to_array(PositionState.LAND,
PositionState)
self.assertEqual([0, 0, 0, 0, 0, 0, 1], array)
self.assertEqual(
PositionState.LAND,
translator.convert_array_to_enum(array, PositionState))
self.assertIsNone(
translator.convert_array_to_enum([0, 0, 0, 0, 0, 0, 0],
PositionState))
def encode_map_examples(examples: List[AntMapExample], channel_count: int) -> Tuple[ndarray, ndarray]:
gst = GameStateTranslator()
if len(examples) == 0: return numpy.empty([0, 43, 39, 7], dtype=int), numpy.empty([0, 5], dtype=int)
ex = examples[0]
features = numpy.zeros([len(examples), ex.row_count, ex.column_count, channel_count], dtype=int)
for e_index, e in enumerate(examples):
for r in range(ex.row_count):
for c in range(ex.column_count):
key = Position(r, c)
features[e_index, r, c] = down_sample(gst, e.features[key], channel_count)
labels = [gst.convert_enum_to_array(ex.label, Direction) for ex in examples]
return numpy.array(features), numpy.array(labels)
def __init__(self, game_identifier: str, name: BotName, model_path: str):
super().__init__(game_identifier, name)
self.game_options: Dict[str, int] = {}
self.game_map: GameMap = None
self.visible_ants: Dict[Position, VisibleAnt] = {}
self.visible_food: Set[Position] = set()
self.visible_hills: Dict[Position, VisibleHill] = {}
self.pending_orders: List[Order] = []
self.channel_count = 7
self.gst = GameStateTranslator()
self.model: Model = keras.models.load_model(model_path)
print(self.model.input.shape)
def map_to_input(
task: Tuple[str, EncodingType, str]) -> Tuple[np.ndarray, np.ndarray]:
bot_name, type, game_path = task
channel_count = 7
gst = GameStateTranslator()
if type == EncodingType.ANT_VISION_2D:
feature_cache_path = game_path.replace(
'.json', f'_ANT_VISION_2D_FEATURES_{bot_name}_{channel_count}.npy')
label_cache_path = game_path.replace(
'.json', f'_ANT_VISION_2D_LABELS_{bot_name}_{channel_count}.npy')
if os.path.exists(feature_cache_path):
return np.load(feature_cache_path), np.load(label_cache_path)
gs = load_game_state(game_path)
try:
ant_vision = gst.convert_to_2d_ant_vision(bot_name, [gs])
features, labels = enc.encode_2d_examples(ant_vision,
channel_count)
print(f'Saving {feature_cache_path}')
np.save(feature_cache_path, features)
np.save(label_cache_path, labels)
return features, labels
except:
print(f'Failed to load ${game_path}')
return np.empty([0, 12, 12, 7]), np.empty([0, 5])
elif type == EncodingType.MAP_2D:
feature_cache_path = game_path.replace(
'.json',
f'_ANT_VISION_2DMAP_FEATURES_{bot_name}_{channel_count}.npy')
label_cache_path = game_path.replace(
'.json',
f'_ANT_VISION_2DMAP_LABELS_{bot_name}_{channel_count}.npy')
if os.path.exists(feature_cache_path):
return np.load(feature_cache_path), np.load(label_cache_path)
gs = load_game_state(game_path)
try:
ant_vision = gst.convert_to_antmap(bot_name, [gs])
features, labels = enc.encode_map_examples(ant_vision,
channel_count)
print(f'Saving {feature_cache_path}')
np.save(feature_cache_path, features)
np.save(label_cache_path, labels)
return features, labels
except:
print(f'Failed to load ${game_path}')
return np.empty([0, 43, 39, 7]), np.empty([0, 5])
else:
raise NotImplementedError()
def decode_map_examples(encoded_examples: Tuple[ndarray, ndarray]) -> List[AntMapExample]:
features, labels = encoded_examples
items = []
gst = GameStateTranslator()
row_count = features.shape[1]
col_count = features.shape[2]
for ex_index in range(features.shape[0]):
example_features: Dict[Position, PositionState] = {}
for row_num in range(features.shape[1]):
for col_num in range(features.shape[2]):
position = Position(row_num, col_num)
enum_val = gst.convert_array_to_enum(features[ex_index, row_num, col_num].tolist(), PositionState)
example_features[position] = enum_val
direction = gst.convert_array_to_enum(labels[ex_index].tolist(), Direction)
items.append(AntMapExample(example_features, direction, row_count, col_count))
return items
def test_encode_2d_map(self):
bot_to_emulate = 'pkmiec_1'
gsg = GameStateGenerator()
gst = GameStateTranslator()
test_game_state = gsg.generate_from_file(self.data_path)
expected_map = gst.convert_to_antmap(bot_to_emulate, [test_game_state])
actual_encoded_map = map_to_input(
(bot_to_emulate, EncodingType.MAP_2D, self.data_path))
actual_decoded_map = decode_map_examples(actual_encoded_map)
for index, expected in enumerate(expected_map):
self.assertEqual(expected.label, actual_decoded_map[index].label)
for expected_pos in expected.features.keys():
self.assertEqual(
expected.features[expected_pos],
actual_decoded_map[index].features[expected_pos])
def profile_encoding():
bot_to_emulate = 'memetix_1'
game_paths = [
f for f in glob.glob(
f'{os.getcwd()}\\training\\tests\\test_data\\**\\*.json')
]
test_games = game_paths[2:3]
gst = GameStateTranslator()
gsg = GameStateGenerator()
pr = cProfile.Profile()
pr.enable()
game_states = seq(test_games).map(
lambda path: load_game_state(path, gsg)).to_list()
mv_features, mv_labels = enc.encode_map_examples(
gst.convert_to_global_antmap(bot_to_emulate, game_states), 7)
pr.disable()
# pr.print_stats(sort='cumtime')
pr.dump_stats('ants_example_python.profile')
def down_sample(gst: GameStateTranslator, ps: PositionState, channel_count: int):
if channel_count == 7:
return gst.convert_enum_to_array(ps, PositionState)
elif channel_count == 3:
if ps == PositionState.FRIENDLY_ANT or ps == PositionState.FRIENDLY_HILL:
return [1, 0, 0]
elif ps == PositionState.HOSTILE_ANT or ps == PositionState.HOSTILE_HILL:
return [0, 1, 0]
elif ps == PositionState.FOOD:
return [0, 0, 1]
else:
return [0, 0, 0]
else:
raise ValueError('Not implemented')
def __init__(self,
game_paths: List[str],
batch_size: int,
bot_to_emulate: str,
channel_count=7,
dataset_type: DatasetType = DatasetType.TRAINING):
self.batch_size = batch_size
self.channel_count = channel_count
self.bot_to_emulate = bot_to_emulate
self.game_paths = sk.utils.shuffle(game_paths)
self.gst = GameStateTranslator()
self.gsg = GameStateGenerator()
self.game_indexes: List[GameIndex] = []
self.loaded_indexes: List[LoadedIndex] = []
self.max_load_count = 10
self.dataset_type = dataset_type
if len(game_paths) == 0:
raise ValueError(
'Must provide at least one game path for the index')
class NNBot(Bot):
def __init__(self, game_identifier: str, name: BotName, model_path: str):
super().__init__(game_identifier, name)
self.game_options: Dict[str, int] = {}
self.game_map: GameMap = None
self.visible_ants: Dict[Position, VisibleAnt] = {}
self.visible_food: Set[Position] = set()
self.visible_hills: Dict[Position, VisibleHill] = {}
self.pending_orders: List[Order] = []
self.channel_count = 7
self.gst = GameStateTranslator()
self.model: Model = keras.models.load_model(model_path)
print(self.model.input.shape)
def start(self, start_data: str):
self.game_options = seq(start_data.split('\n')) \
.filter(lambda line: line != '') \
.map(lambda opt: (opt.split(' ')[0], int(opt.split(' ')[1]))) \
.to_dict()
self.game_map = create_map(self.game_options['rows'],
self.game_options['cols'])
def convert_pos_to_state(self, pos: Position) -> PositionState:
if self.visible_ants.get(pos) is not None:
return PositionState.FRIENDLY_ANT if self.visible_ants[
pos].is_friendly() else PositionState.HOSTILE_ANT
if self.visible_hills.get(pos) is not None:
return PositionState.FRIENDLY_HILL if self.visible_hills.get(
pos).is_friendly() else PositionState.HOSTILE_HILL
if pos in self.visible_food:
return PositionState.FOOD
return PositionState.WATER if self.game_map.get_terrain(
pos) == TerrainType.WATER else PositionState.LAND
def create_predictions(
self,
ants: List[VisibleAnt]) -> List[Tuple[VisibleAnt, Direction]]:
def map_to_position_state(
ant: VisibleAnt) -> Dict[Position, PositionState]:
ant_vision = self.game_map.get_positions_within_distance(
ant.position,
self.game_options['viewradius2'],
use_absolute=False,
crop_to_square=True)
position_states = {
av: self.convert_pos_to_state(
self.game_map.wrap_position(
ant.position.row + av.row,
ant.position.column + av.column))
for av in ant_vision
}
return position_states
def convert_prediction_to_direction(
ant: VisibleAnt,
prediction: List) -> Tuple[VisibleAnt, Direction]:
pred = [0] * 5
pred[numpy.array(prediction).argmax()] = 1
d: Direction = self.gst.convert_array_to_enum(pred, Direction)
return ant, d
position_states = seq(ants).map(map_to_position_state).to_list()
features = encode_2d_features(position_states, self.gst,
self.channel_count)
predictions = self.model.predict(features)
mapped_predictions = [
convert_prediction_to_direction(ants[index], prediction)
for index, prediction in enumerate(predictions)
]
return mapped_predictions
def create_orders(self) -> List[Order]:
friendly_ants = seq(self.visible_ants.values()) \
.filter(lambda a: a.is_friendly()) \
.to_list()
pending_orders = seq(friendly_ants) \
.map(lambda va: Order(va.position, Direction.NONE,
self.game_map.adjacent_movement_position(va.position, Direction.NONE))) \
.to_list()
predictions: List[Tuple[
VisibleAnt, Direction]] = self.create_predictions(friendly_ants)
pass_through_count = 0
while seq(pending_orders).filter(
lambda po: po.dir == Direction.NONE).len(
) > 0 and pass_through_count < 3:
for index, order in enumerate(pending_orders):
# pylint: disable=cell-var-from-loop
matching_prediction = seq(predictions).find(
lambda t: t[0].position == order.position)
new_order_position = self.game_map.adjacent_movement_position(
order.position, matching_prediction[1])
# pylint: disable=cell-var-from-loop
conflicting_order = seq(pending_orders).find(
lambda po: po.next_position == new_order_position)
if conflicting_order is None:
pending_orders[index] = Order(order.position,
matching_prediction[1],
new_order_position)
pass_through_count += 1
return pending_orders
def play_turn(self, play_turn_data: str):
def parse_segments(line: str) -> Tuple[str, Position, Optional[int]]:
segments = line.split(' ')
return segments[0], Position(int(segments[1]), int(
segments[2])), int(segments[3]) if len(segments) == 4 else None
input_data = seq(play_turn_data.split('\n')) \
.filter(lambda line: line != '') \
.map(parse_segments) \
.to_list()
seq(input_data).filter(lambda t: t[0] == 'w').for_each(
lambda t: self.game_map.update_terrain(t[1], TerrainType.WATER))
self.visible_hills = seq(input_data).filter(lambda t: t[0] == 'h').map(
lambda t: (t[1], VisibleHill(t[1], t[2]))).to_dict()
self.visible_ants = seq(input_data).filter(lambda t: t[0] == 'a').map(
lambda t: (t[1], VisibleAnt(t[1], t[2]))).to_dict()
self.visible_food = seq(input_data).filter(lambda t: t[0] == 'f').map(
lambda t: t[1]).to_set()
self.pending_orders = self.create_orders()
def read_lines(self):
orders = seq(self.pending_orders).map(str).to_list()
return orders
def test_create_2d_ant_vision(self):
game_state = create_test_game_state()
translator = GameStateTranslator()
translated = translator.convert_to_2d_ant_vision(
'pkmiec_1', [game_state])
self.assertIsNotNone(translated)
def encode_2d_examples(examples: List[AntVision2DExample], channel_count: int) -> Tuple[ndarray, ndarray]:
gst = GameStateTranslator()
features = encode_2d_features(seq(examples).map(lambda e: e.features).to_list(), gst, channel_count)
labels = numpy.array([gst.convert_enum_to_array(ex.label, Direction) for ex in examples])
return features, labels
import cProfile
import glob
from ants_ai.training.neural_network.encoders.game_state_translator import GameStateTranslator
from ants_ai.training.tests.test_utils import create_test_game_state
from ants_ai.training.game_state.generator import GameStateGenerator
from functional import seq
import jsonpickle
import os
from ants_ai.training.game_state.game_state import GameState
from ants_ai.training.neural_network.encoders import encoders as enc
game_state = create_test_game_state()
translator = GameStateTranslator()
def load_game_state(path: str, gsg: GameStateGenerator) -> GameState:
with open(path, "r") as f:
json_data = f.read()
pr = jsonpickle.decode(json_data)
return gsg.generate(pr)
def convert_game_state():
bot_to_emulate = 'memetix_1'
game_paths = [
f for f in glob.glob(
f'{os.getcwd()}\\training\\tests\\test_data\\**\\*.json')
]
test_games = game_paths[2:3]
gst = GameStateTranslator()
