def load_game_state(self):
print(f"turn number = {self.turn_number}")
self.game.state = GameState.from_csv_line(
self.game_states[self.turn_number])
next_action = self.game_states[self.turn_number].split(',')[-1].strip()
print(f"action from this state: {next_action}")
self.draw_game_tiles()
self.draw_score()
def main(model_h5_file: str, game_file: str):
print(f"==== Loading model from {model_h5_file} ====")
value_model = load_model(model_h5_file)
with open(game_file, 'r') as f:
lines = f.readlines()
game_states = [GameState.from_csv_line(line) for line in lines]
for current_state in game_states:
network_input = np.expand_dims(convert_tiles_to_bitarray(
current_state.tiles),
axis=0)
network_output = value_model.predict(network_input)[0]
assert len(network_output) == 4
# print(f"network output: {network_output}")
action = np.argmax(network_output)
print(pretty_print_tiles(current_state))
print("selected action:", ACTIONS[action])
print("---")
def main():
parser = argparse.ArgumentParser(description='Play N games using the random bot.')
parser.add_argument('game_dir', type=str,
help='directory where games are saved to')
parser.add_argument('figure_prefix', type=str,
help='prefix of analysis figure filenames (saved to the figures directory)')
args = parser.parse_args()
files = glob.glob(f"{args.game_dir}/*.csv")
random_seeds = []
scores = []
game_lens = []
max_tile_values = []
for filepath in files:
head, filename = os.path.split(filepath)
root, ext = os.path.splitext(filename)
if len(ext) > 0:
date, random_seed = root.split('_', 1)
if random_seed in random_seeds:
print(f"Random seed {random_seed} already used!")
random_seeds.append(random_seed)
else:
print(f"Unable to parse filename extension")
with open(filepath, 'r') as f:
lines = f.readlines()
final_game_state = GameState.from_csv_line(lines[-1])
if not final_game_state.game_over:
print(f"Game not over for {filepath}")
continue
max_tile_value = final_game_state.max_tile_value()
max_tile_values.append(max_tile_value)
scores.append(final_game_state.score)
game_lens.append(len(lines))
scores = np.array(scores, dtype=np.uint)
game_lens = np.array(game_lens, dtype=np.uint)
max_tile_values = np.array(max_tile_values, dtype=np.uint)
# print(scores)
with open(f"{args.figure_prefix}_summary.txt", 'w') as f:
# f.write(f"length of scores = {len(scores)}\n")
f.write(f"max in scores = {np.amax(scores)}\n")
f.write(f"max score filename = {files[np.argmax(scores)]}\n")
f.write(f"min in scores = {np.amin(scores)}\n")
f.write(f"min score filename = {files[np.argmin(scores)]}\n")
f.write(f"mean in scores = {np.mean(scores)}\n")
f.write(f"median in scores = {np.median(scores)}\n")
# f.write(f"length of game_lens = {len(game_lens)}\n")
f.write(f"max in game_lens = {np.amax(game_lens)}\n")
f.write(f"longest game filename = {files[np.argmax(game_lens)]}\n")
f.write(f"min in game_lens = {np.amin(game_lens)}\n")
f.write(f"shortest game filename = {files[np.argmin(game_lens)]}\n")
f.write(f"mean in game_lens = {np.mean(game_lens)}\n")
f.write(f"median in game_lens = {np.median(game_lens)}\n")
fig, ax = plt.subplots()
ax.hist(scores, bins='auto')
ax.set_xlabel("Final Scores")
ax.set_ylabel("Number of Games")
ax.set_title(f"{args.figure_prefix} Bot: Final Scores Distribution")
fig.savefig(f"figures/{args.figure_prefix}_scores_hist.png")
plt.close(fig)
fig, ax = plt.subplots()
ax.hist(game_lens, bins='auto')
ax.set_xlabel("Game Lengths (# of Turns)")
ax.set_ylabel("Number of Games")
ax.set_title(f"{args.figure_prefix} Bot: Game Lengths Distribution")
fig.savefig(f"figures/{args.figure_prefix}_game_lens_hist.png")
plt.close(fig)
fig, ax = plt.subplots()
tile_values, tile_freqs = np.unique(max_tile_values, return_counts=True)
ax.bar(range(len(tile_values)), tile_freqs)
plt.xticks(range(len(tile_values)), tile_values)
ax.set_xlabel("Final Max Tile Value")
ax.set_ylabel("Number of Games")
ax.set_title(f"{args.figure_prefix} Bot: Final Max Tile Distribution")
fig.savefig(f"figures/{args.figure_prefix}_max_tile_values_hist.png")
plt.close(fig)
fig, ax = plt.subplots()
ax.scatter(game_lens, scores)
ax.set_xlabel("Game Lengths (# of Turns)")
ax.set_ylabel("Final Scores")
ax.set_title(f"{args.figure_prefix} Bot: Score vs. Game Length")
fig.savefig(f"figures/{args.figure_prefix}_score_vs_game_len.png")
plt.close(fig)
fig, ax = plt.subplots()
max_tile_idxs = {}
for i in range(len(max_tile_values)):
tile_value = max_tile_values[i]
if tile_value not in max_tile_idxs:
max_tile_idxs[tile_value] = []
max_tile_idxs[tile_value].append(i)
data = [scores[max_tile_idxs[tile_value]] for tile_value in sorted(max_tile_idxs.keys())]
# print(data)
ax.boxplot(data, labels=sorted(max_tile_idxs.keys()))
ax.set_xlabel("Final Max Tile Value")
ax.set_ylabel("Final Scores")
ax.set_title(f"{args.figure_prefix} Bot: Score vs. Final Max Tile Value")
fig.savefig(f"figures/{args.figure_prefix}_score_vs_max_tile_value.png")
plt.close(fig)
fig, ax = plt.subplots()
ax.scatter(random_seeds, scores)
ax.set_xlabel("Random Seeds")
ax.set_ylabel("Final Scores")
ax.set_title(f"{args.figure_prefix} Bot: Score vs. Random Seeds")
fig.savefig(f"figures/{args.figure_prefix}_score_vs_random_seed.png")
plt.close(fig)