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0ab2566 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 | import sys
import os
import numpy as np
import torch
def get_topn_same_recall(score, labels, N, threshold = 0.0):
gt_num = list(labels).count(0)
hit = 0.0
for i in range(min(N, len(labels))):
if labels[i] == 0 and score[i] >= threshold:
hit += 1
if gt_num == 0 or N == 0:
return 0.0
return hit / min(N, gt_num)
def get_topn_hit_rate(labels, N):
"""HR@N: Top-N中是否存在至少一个正样本(label==0)，是则为1，否则为0"""
for i in range(min(N, len(labels))):
if labels[i] == 0:
return 1.0
return 0.0
def TopNHitRate(query_dic, TopN):
"""计算所有query的平均HR@N"""
hr_sum = 0.0
query_num = 0.0
for _, data in query_dic.items():
query_score = np.array(data['score'])
query_label = np.array(data['label'])
index_score = np.argsort(-query_score)
query_label_sort = query_label[index_score]
if 0 in query_label:
hr_sum += get_topn_hit_rate(query_label_sort, TopN)
query_num += 1
if query_num == 0:
return 0.0
return hr_sum / query_num
def TopNAcc(query_dic, TopN):
same_ap = 0.0
same_query_num = 0.0
for _, data in query_dic.items():
query_score = np.array(data['score'])
query_label = np.array(data['label'])
index_score = np.argsort(-query_score)
query_score_sort = query_score[index_score]
query_label_sort = query_label[index_score]
if 0 in query_label:
same_ap += get_topn_same_recall(query_score_sort, query_label_sort, TopN)
same_query_num += 1
if same_query_num == 0:
return 0.0
return same_ap / same_query_num
def tongkuan_score_print(query_dic_all, sim_weight):
results_all = []
results_all.append(['same_Top1','same_Top5','same_Top10', 'same_Top20','same_Top30','sim', 'HR@1', 'HR@5', 'HR@10', 'HR@20'])
query_score_dic = {}
for query, value in query_dic_all.items():
query_score_dic[query] = {}
query_score_dic[query]['score'] = value['sim']
query_score_dic[query]['label'] = value['label']
# 基于上面的数据，去算相似率
same_rate_Top1 = TopNAcc(query_score_dic, 1)
same_rate_Top5 = TopNAcc(query_score_dic, 5)
same_rate_Top10 = TopNAcc(query_score_dic, 10)
same_rate_Top20 = TopNAcc(query_score_dic, 20)
same_rate_Top30 = TopNAcc(query_score_dic, 30)
sim = sim_weight[0] * same_rate_Top1 + sim_weight[1] * same_rate_Top5 + sim_weight[2] * same_rate_Top10 + sim_weight[3] * same_rate_Top20 + sim_weight[4] * same_rate_Top30
hr_1 = TopNHitRate(query_score_dic, 1)
hr_5 = TopNHitRate(query_score_dic, 5)
hr_10 = TopNHitRate(query_score_dic, 10)
hr_20 = TopNHitRate(query_score_dic, 20)
print ('##### wo margin #####')
print ('same_rate_Top1: ', same_rate_Top1)
print ('same_rate_Top5: ', same_rate_Top5)
print ('same_rate_Top10: ', same_rate_Top10)
print ('same_rate_Top20: ', same_rate_Top20)
print ('same_rate_Top30: ', same_rate_Top30)
print ('sim: ', sim)
print ('HR@1: ', hr_1)
print ('HR@5: ', hr_5)
print ('HR@10: ', hr_10)
print ('HR@20: ', hr_20)
results_all.append([same_rate_Top1, same_rate_Top5, same_rate_Top10, same_rate_Top20, same_rate_Top30, sim, hr_1, hr_5, hr_10, hr_20])
results_all_dict = {}
for i in range(len(results_all[0])):
results_all_dict[results_all[0][i]] = results_all[1][i]
return results_all_dict |