calculate.py 3.8 KB

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  1. """
  2. @author: luojunhui
  3. """
  4. import json
  5. import os
  6. from applications.log import logging
  7. def read_single_file(filename):
  8. """
  9. :param filename:
  10. """
  11. with open(filename, encoding="utf-8") as f:
  12. data = json.loads(f.read())
  13. if data:
  14. return data
  15. else:
  16. return {}
  17. def compute_similarity(file_1, file_2):
  18. """
  19. 计算
  20. :param file_1:
  21. :param file_2:
  22. :return:
  23. """
  24. data_1 = read_single_file(file_1)
  25. data_2 = read_single_file(file_2)
  26. def calculate_v1(d1, d2):
  27. """
  28. 通过交并集来判断
  29. :param d1:
  30. :param d2:
  31. :return:
  32. """
  33. f1_keys = set(d1["key_words"])
  34. f2_keys = set(d2["key_words"])
  35. keys_union = f1_keys | f2_keys
  36. keys_intersection = f1_keys & f2_keys
  37. f1_search_keys = set(d1["search_keys"])
  38. f2_search_keys = set(d2["search_keys"])
  39. search_keys_union = f1_search_keys | f2_search_keys
  40. search_keys_intersection = f1_search_keys & f2_search_keys
  41. f1_extra_keys = set(d1["extra_keys"])
  42. f2_extra_keys = set(d2["extra_keys"])
  43. extra_keys_union = f1_extra_keys | f2_extra_keys
  44. extra_keys_intersection = f1_extra_keys & f2_extra_keys
  45. score_1 = len(keys_intersection) / len(keys_union)
  46. score_2 = len(search_keys_intersection) / len(search_keys_union)
  47. score_3 = len(extra_keys_intersection) / len(extra_keys_union)
  48. return score_1 * 0.4 + score_2 * 0.4 + score_3 * 0.2
  49. def calculate_v2(d1, d2):
  50. """
  51. 计算方法 v2
  52. :param d1:
  53. :param d2:
  54. :return:
  55. """
  56. score = 0
  57. tone_1 = d1["tone"]
  58. tone_2 = d2["tone"]
  59. if tone_1 == tone_2:
  60. score += 0.1
  61. target_audience_1 = d1["target_audience"]
  62. target_audience_2 = d2["target_audience"]
  63. if target_audience_1 == target_audience_2:
  64. score += 0.2
  65. target_age_1 = d1["target_age"]
  66. target_age_2 = d2["target_age"]
  67. if target_age_1 == target_age_2:
  68. score += 0.2
  69. address_1 = d1["address"]
  70. address_2 = d2["address"]
  71. if address_1 == address_2:
  72. score += 0.2
  73. gender_1 = d1["theme"]
  74. gender_2 = d2["theme"]
  75. if gender_1 == gender_2:
  76. score += 0.5
  77. return score
  78. if data_1 and data_2:
  79. try:
  80. score_1 = calculate_v1(data_1, data_2)
  81. score_2 = calculate_v2(data_1, data_2)
  82. return score_1, score_2
  83. except Exception as e:
  84. return 0, 0
  85. else:
  86. return 0, 0
  87. def title_mix(title_p, dt, trace_id):
  88. """
  89. 执行代码
  90. :param trace_id: 请求唯一 id
  91. :param title_p:
  92. :param dt: dt
  93. """
  94. json_path = os.path.join(os.getcwd(), 'applications', 'static', dt)
  95. # 处理标题信息
  96. files = os.listdir(json_path)
  97. pq_files = [os.path.join(json_path, file) for file in files]
  98. score_list_1 = []
  99. score_list_2 = []
  100. for file in pq_files:
  101. file_name = file.split('/')[-1].replace(".json", "")
  102. v_id = file_name.split('_')[1]
  103. uid = file_name.split('_')[0]
  104. score1, score2 = compute_similarity(title_p, file)
  105. score_list_1.append([score1, v_id, uid])
  106. score_list_2.append([score2, v_id, uid])
  107. s1_list = sorted(score_list_1, key=lambda x: x[0], reverse=True)
  108. s2_list = sorted(score_list_2, key=lambda x: x[0], reverse=True)
  109. title = title_p.split("/")[-1].replace(".json", "")
  110. obj = {
  111. "title": title,
  112. "s1_vid": s1_list[0][1],
  113. "s1_score": s1_list[0][0],
  114. "s1_uid": s1_list[0][2],
  115. "s2_vid": s2_list[0][1],
  116. "s2_score": s2_list[0][0],
  117. "s2_uid": s2_list[0][2]
  118. }
  119. logging(
  120. code="1003",
  121. info="计算结果得分",
  122. data=obj,
  123. function="title_mix",
  124. trace_id=trace_id
  125. )
  126. return obj