def accumulate_stats()

in train_helpers.py [0:0]

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

def accumulate_stats(stats, frequency):
    z = {}
    for k in stats[-1]:
        if k in ['distortion_nans', 'rate_nans', 'skipped_updates', 'gcskip']:
            z[k] = np.sum([a[k] for a in stats[-frequency:]])
        elif k == 'grad_norm':
            vals = [a[k] for a in stats[-frequency:]]
            finites = np.array(vals)[np.isfinite(vals)]
            if len(finites) == 0:
                z[k] = 0.0
            else:
                z[k] = np.max(finites)
        elif k == 'elbo':
            vals = [a[k] for a in stats[-frequency:]]
            finites = np.array(vals)[np.isfinite(vals)]
            z['elbo'] = np.mean(vals)
            z['elbo_filtered'] = np.mean(finites)
        elif k == 'iter_time':
            z[k] = stats[-1][k] if len(stats) < frequency else np.mean([a[k] for a in stats[-frequency:]])
        else:
            z[k] = np.mean([a[k] for a in stats[-frequency:]])
    return z