stats.py 文件源码

def merge_filtered_metrics(filtered_metrics):
    result = {
        'filtered_bcs': 0,
        'filtered_bcs_lb': 0,
        'filtered_bcs_ub': 0,
        'max_filtered_bcs': 0,
        'filtered_bcs_var': 0,
        'filtered_bcs_cv': 0,
    }
    for i, fm in enumerate(filtered_metrics):
        # Add per-gem group metrics
        result.update({'gem_group_%d_%s' % (i + 1, key): value for key, value in fm.iteritems()})

        # Compute metrics over all gem groups
        result['filtered_bcs'] += fm['filtered_bcs']
        result['filtered_bcs_lb'] += fm['filtered_bcs_lb']
        result['filtered_bcs_ub'] += fm['filtered_bcs_ub']
        result['max_filtered_bcs'] += fm['max_filtered_bcs']
        result['filtered_bcs_var'] += fm['filtered_bcs_var']

    # Estimate CV based on sum of variances and means
    result['filtered_bcs_cv'] = tk_stats.robust_divide(
        np.sqrt(result['filtered_bcs_var']), fm['filtered_bcs'])

    return result