def split_test_training(positions_w_context, est_num_positions):
print("Estimated number of chunks: %s" % (est_num_positions // CHUNK_SIZE))
desired_test_size = 10**5
if est_num_positions < 2 * desired_test_size:
positions_w_context = list(positions_w_context)
test_size = len(positions_w_context) // 3
return positions_w_context[:test_size], [
positions_w_context[test_size:]
]
else:
shuffled_positions = utils.shuffler(positions_w_context)
test_chunk = utils.take_n(desired_test_size, shuffled_positions)
training_chunks = utils.iter_chunks(CHUNK_SIZE, shuffled_positions)
return test_chunk, training_chunks
def extract_moves(final_positions):
winning_moves = []
losing_moves = []
for final_position in final_positions:
positions_w_context = utils.take_n(
strategies.POLICY_CUTOFF_DEPTH,
sgf_wrapper.replay_position(final_position,extract_move_probs=True))
winner = utils.parse_game_result(final_position.result())
for pwc in positions_w_context:
if pwc.position.to_play == winner:
winning_moves.append(pwc)
else:
losing_moves.append(pwc)
return load_data_sets.DataSet.from_positions_w_context(winning_moves,extract_move_prob=True),\
load_data_sets.DataSet.from_positions_w_context(losing_moves,extract_move_prob=True)
def extract_moves(final_positions):
winning_moves = []
losing_moves = []
#logger.debug(f'Game final positions{final_positions}')
for final_position in final_positions:
positions_w_context = utils.take_n(
POLICY_CUTOFF_DEPTH,
sgf_wrapper.replay_position(final_position,
extract_move_probs=True))
winner = utils.parse_game_result(final_position.result())
#logger.debug(f'positions_w_context length: {len(positions_w_context)}')
for pwc in positions_w_context:
if pwc.position.to_play == winner:
winning_moves.append(pwc)
else:
losing_moves.append(pwc)
return load_data_sets.DataSet.from_positions_w_context(winning_moves, extract_move_prob=True),\
load_data_sets.DataSet.from_positions_w_context(losing_moves, extract_move_prob=True)