Load the Robot Execution Failures LP1 Data Set[1].
    The Time series are passed as a flat DataFrame.

    Examples
    ========

    >>> from tsfresh.examples import load_robot_execution_failures
    >>> df, y = load_robot_execution_failures()
    >>> print(df.shape)
    (1320, 8)

    :param multiclass: If True, return all target labels. The default returns only "normal" vs all other labels.
    :type multiclass: bool
    :return: time series data as :class:`pandas.DataFrame` and target vector as :class:`pandas.Series`
    :rtype: tuple

def load_robot_execution_failures(multiclass=False):
    """
    Load the Robot Execution Failures LP1 Data Set[1].
    The Time series are passed as a flat DataFrame.

    Examples
    ========

    >>> from tsfresh.examples import load_robot_execution_failures
    >>> df, y = load_robot_execution_failures()
    >>> print(df.shape)
    (1320, 8)

    :param multiclass: If True, return all target labels. The default returns only "normal" vs all other labels.
    :type multiclass: bool
    :return: time series data as :class:`pandas.DataFrame` and target vector as :class:`pandas.Series`
    :rtype: tuple
    """
    if not os.path.exists(data_file_name):
        raise RuntimeError(UCI_MLD_REF_MSG)

    id_to_target = {}
    df_rows = []

    with open(data_file_name) as f:
        cur_id = 0
        time = 0

        for line in f.readlines():
            # New sample --> increase id, reset time and determine target
            if line[0] not in ['\t', '\n']:
                cur_id += 1
                time = 0
                if multiclass:
                    id_to_target[cur_id] = line.strip()
                else:
                    id_to_target[cur_id] = (line.strip() == 'normal')
            # Data row --> split and convert values, create complete df row
            elif line[0] == '\t':
                values = list(map(int, line.split('\t')[1:]))
                df_rows.append([cur_id, time] + values)
                time += 1

    df = pd.DataFrame(df_rows, columns=['id', 'time', 'F_x', 'F_y', 'F_z', 'T_x', 'T_y', 'T_z'])
    y = pd.Series(id_to_target)

    return df, y