speech_eval_old.py 文件源码

def get_pr(reference_frames,output_frames,mode='type',pr_resolution=100):

    # filter output by confidence
    confidence = collect_confidence(output_frames)
    conf_order = []
    step = 100/float(pr_resolution)
    for j in range(1,pr_resolution+1):
        conf_order.append( np.percentile(confidence, j*step) )
    conf_order = [-1] + conf_order + [2]

    # get curve
    params = []
    for threshold in conf_order:
        params.append( [ reference_frames, output_frames, confidence, threshold, mode ] )
    all_tp, all_fp, all_fn, all_prec, all_rec = zip(*pool.map(single_point, params))
    all_prec = list(all_prec) #+ [0]
    all_rec  = list(all_rec) #+ [1]

    all_rec, all_prec = zip(*sorted(zip(all_rec, all_prec)))
    AUC = metrics.auc(all_rec, all_prec)

    return all_rec, all_prec, AUC


# create complete output for 1 mode --------------------------------------------