Line #    Mem usage    Increment  Occurrences   Line Contents
=============================================================
    37    277.3 MiB    277.3 MiB           1   @profile
    38                                         def load():
    39    277.3 MiB      0.0 MiB           1       train_data = np.empty((0, 833))  # 832+1 is the size of the flattened board in one hot encoding and move_turn is one feature
    40    277.3 MiB      0.0 MiB           1       train_target = []
    41    818.6 MiB    -27.5 MiB           4       for file in files:
    42    680.7 MiB      0.0 MiB           6           with open(file, "r") as f:
    43    680.7 MiB      0.1 MiB           3               contents = f.read()
    44    680.7 MiB      0.1 MiB           3           contents = contents.split(" \n")
    45    846.1 MiB  -1489.8 MiB         303           for game in tqdm.tqdm(contents, desc="each game"):
    46    846.1 MiB  -1353.6 MiB         300               game = game.split(" \n")
    47    846.1 MiB  -1353.6 MiB         300               g = "".join(game)
    48    846.1 MiB  -1353.6 MiB         300               game = g.split(" ")
    49    846.1 MiB -101971.1 MiB       26530               game = [x for x in game if x]  # remove empty strings
    50    846.1 MiB  -1353.6 MiB         300               MAX_MOMENTS = min(20, len(game))
    51    846.1 MiB -29509.5 MiB        6566               unsampled_idx = [np.random.randint(1, len(game)) for _ in range(MAX_MOMENTS - 1)]
    52    847.1 MiB -29603.5 MiB        5966               for move in range(MAX_MOMENTS - 1):
    53    847.1 MiB -28232.5 MiB        5666                   board = chess.Board()
    54    847.1 MiB -28232.5 MiB        5666                   up_to = unsampled_idx[move]
    55    847.1 MiB -28232.5 MiB        5666                   moment = game[:up_to]
    56    847.1 MiB -1073576.1 MiB      252258                   for counter, move in enumerate(moment):
    57    847.1 MiB -1045299.8 MiB      246592                       move = Move.from_uci(move)
    58    847.1 MiB -1045334.6 MiB      246592                       board.push(move)
    59    847.1 MiB -28417.3 MiB        5666                   matrix_game = np.array([board_data(board)])  # game after one hot encoding
    60                                                         # Add move_turn as a feature to matrix_game
    61    847.1 MiB -28275.1 MiB        5666                   move_turn = counter % 2
    62    847.1 MiB -28275.9 MiB        5666                   matrix_game = np.concatenate((matrix_game, np.array([[move_turn]])), axis=1)
    63    847.1 MiB -27551.0 MiB        5666                   train_data = np.concatenate((train_data, matrix_game), axis=0)
    64    847.1 MiB -28249.4 MiB        5666                   status = 0
    65    847.1 MiB -28249.4 MiB        5666                   if (move_turn == 0 and file == "./fracww.pgn") or (
    66    847.1 MiB -27275.9 MiB        4733                           move_turn == 1 and file == "./fracbw.pgn"):
    67    680.7 MiB -10114.0 MiB        1859                       status = 1
    68    847.1 MiB -18089.1 MiB        3807                   elif (move_turn == 0 and file == "./fracbw.pgn") or (
    69    847.1 MiB  -7747.9 MiB        2845                           move_turn == 1 and file == "./fracww.pgn"):
    70    680.6 MiB -11276.1 MiB        1907                       status = -1
    71    847.1 MiB -28106.1 MiB        5666                   train_target.append(status)
    72    818.6 MiB      0.0 MiB           1       return train_data, train_target