def create_stacked_bonus_plots()

in src/minmaxML.py [0:0]

• 13 lines of code
• 3 McCabe index (conditional complexity)

def create_stacked_bonus_plots(num_group_types, extra_error_types, numgroups, specific_errors, index, groupsize,
                               total_steps):
    bonus_plots = []
    for err_type in extra_error_types:
        # Determine pop error type
        if err_type in ['FP', 'FN', 'FP-Log-Loss', 'FN-Log-Loss']:
            pop_err_type = '0/1 Loss'
        else:
            pop_err_type = err_type

        grp_errs = compute_stacked_mixture_group_errors(num_group_types, numgroups, specific_errors[err_type], index,
                                                        groupsize, total_steps)
        pop_errs = compute_mixture_pop_errors(specific_errors[pop_err_type], total_steps)
        bonus_plots.append((err_type, grp_errs, pop_errs, pop_err_type))
    return bonus_plots