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2025-03-12
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south-forecast.2025-03-12.0.log

south-forecast.2025-03-12.0.log 16 KB
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  1. 2025-03-12 08:33:54,280 - logs.py - INFO - 日志输出路径为：E:\compete\app\logs - getLogName
    2. 

  2. 2025-03-12 08:34:12,481 - logs.py - INFO - 日志输出路径为：E:\compete\app\logs - getLogName
    3. 

  3. 2025-03-12 08:34:44,794 - logs.py - INFO - 日志输出路径为：E:\compete\app\logs - getLogName
    4. 

  4. 2025-03-12 08:34:48,826 - logs.py - INFO - 日志输出路径为：E:\compete\app\logs - getLogName
    5. 

  5. 2025-03-12 08:35:18,502 - logs.py - INFO - 日志输出路径为：E:\compete\app\logs - getLogName
    6. 

  6. 2025-03-12 08:36:58,641 - logs.py - INFO - 日志输出路径为：E:\compete\app\logs - getLogName
    7. 

  7. 2025-03-12 08:37:56,560 - logs.py - INFO - 日志输出路径为：E:\compete\app\logs - getLogName
    8. 

  8. 2025-03-12 08:38:35,572 - logs.py - INFO - 日志输出路径为：E:\compete\app\logs - getLogName
    9. 

  9. 2025-03-12 08:39:21,276 - logs.py - INFO - 日志输出路径为：E:\compete\app\logs - getLogName
    10. 

  10. 2025-03-12 08:40:49,875 - logs.py - INFO - 日志输出路径为：E:\compete\app\logs - getLogName
    11. 

  11. 2025-03-12 08:41:26,419 - logs.py - INFO - 日志输出路径为：E:\compete\app\logs - getLogName
    12. 

  12. 2025-03-12 08:42:36,685 - logs.py - INFO - 日志输出路径为：E:\compete\app\logs - getLogName
    13. 

  13. 2025-03-12 08:44:24,362 - logs.py - INFO - 日志输出路径为：E:\compete\app\logs - getLogName
    14. 

  14. 2025-03-12 08:45:06,742 - logs.py - INFO - 日志输出路径为：E:\compete\app\logs - getLogName
    15. 

  15. 2025-03-12 08:45:06,852 - tf_lstm_train.py - INFO - Program starts execution! - model_training
    16. 

  16. 2025-03-12 08:45:06,852 - tf_lstm_train.py - INFO - Program starts execution! - model_training
    17. 

  17. 2025-03-12 08:45:06,864 - data_cleaning.py - INFO - 开始清洗：训练集…… - cleaning
    18. 

  18. 2025-03-12 08:45:06,864 - data_cleaning.py - INFO - 开始清洗：训练集…… - cleaning
    19. 

  19. 2025-03-12 08:45:06,870 - data_cleaning.py - INFO - 行清洗：清洗的行数有：881，缺失的列有： - key_field_row_cleaning
    20. 

  20. 2025-03-12 08:45:06,870 - data_cleaning.py - INFO - 行清洗：清洗的行数有：881，缺失的列有： - key_field_row_cleaning
    21. 

  21. 2025-03-12 08:45:06,881 - data_handler.py - INFO - 2022-05-05 23:45:00 ~ 2022-05-09 00:00:00 - missing_time_splite
    22. 

  22. 2025-03-12 08:45:06,881 - data_handler.py - INFO - 2022-05-05 23:45:00 ~ 2022-05-09 00:00:00 - missing_time_splite
    23. 

  23. 2025-03-12 08:45:06,881 - data_handler.py - INFO - 缺失点数：288.0 - missing_time_splite
    24. 

  24. 2025-03-12 08:45:06,881 - data_handler.py - INFO - 缺失点数：288.0 - missing_time_splite
    25. 

  25. 2025-03-12 08:45:06,914 - data_handler.py - INFO - 2022-06-25 07:45:00 ~ 2022-06-25 08:15:00 - missing_time_splite
    26. 

  26. 2025-03-12 08:45:06,914 - data_handler.py - INFO - 2022-06-25 07:45:00 ~ 2022-06-25 08:15:00 - missing_time_splite
    27. 

  27. 2025-03-12 08:45:06,915 - data_handler.py - INFO - 缺失点数：1.0 - missing_time_splite
    28. 

  28. 2025-03-12 08:45:06,915 - data_handler.py - INFO - 缺失点数：1.0 - missing_time_splite
    29. 

  29. 2025-03-12 08:45:06,915 - data_handler.py - INFO - 需要补值的点数：1.0 - missing_time_splite
    30. 

  30. 2025-03-12 08:45:06,915 - data_handler.py - INFO - 需要补值的点数：1.0 - missing_time_splite
    31. 

  31. 2025-03-12 08:45:06,916 - data_handler.py - INFO - 2022-06-27 06:45:00 ~ 2022-06-27 07:15:00 - missing_time_splite
    32. 

  32. 2025-03-12 08:45:06,916 - data_handler.py - INFO - 2022-06-27 06:45:00 ~ 2022-06-27 07:15:00 - missing_time_splite
    33. 

  33. 2025-03-12 08:45:06,917 - data_handler.py - INFO - 缺失点数：1.0 - missing_time_splite
    34. 

  34. 2025-03-12 08:45:06,917 - data_handler.py - INFO - 缺失点数：1.0 - missing_time_splite
    35. 

  35. 2025-03-12 08:45:06,917 - data_handler.py - INFO - 需要补值的点数：1.0 - missing_time_splite
    36. 

  36. 2025-03-12 08:45:06,917 - data_handler.py - INFO - 需要补值的点数：1.0 - missing_time_splite
    37. 

  37. 2025-03-12 08:45:06,920 - data_handler.py - INFO - 2022-07-01 06:30:00 ~ 2022-07-01 23:45:00 - missing_time_splite
    38. 

  38. 2025-03-12 08:45:06,920 - data_handler.py - INFO - 2022-07-01 06:30:00 ~ 2022-07-01 23:45:00 - missing_time_splite
    39. 

  39. 2025-03-12 08:45:06,920 - data_handler.py - INFO - 缺失点数：68.0 - missing_time_splite
    40. 

  40. 2025-03-12 08:45:06,920 - data_handler.py - INFO - 缺失点数：68.0 - missing_time_splite
    41. 

  41. 2025-03-12 08:45:06,925 - data_handler.py - INFO - 2022-07-08 06:15:00 ~ 2022-07-08 23:45:00 - missing_time_splite
    42. 

  42. 2025-03-12 08:45:06,925 - data_handler.py - INFO - 2022-07-08 06:15:00 ~ 2022-07-08 23:45:00 - missing_time_splite
    43. 

  43. 2025-03-12 08:45:06,925 - data_handler.py - INFO - 缺失点数：69.0 - missing_time_splite
    44. 

  44. 2025-03-12 08:45:06,925 - data_handler.py - INFO - 缺失点数：69.0 - missing_time_splite
    45. 

  45. 2025-03-12 08:45:06,926 - data_handler.py - INFO - 2022-07-09 06:15:00 ~ 2022-07-09 23:45:00 - missing_time_splite
    46. 

  46. 2025-03-12 08:45:06,926 - data_handler.py - INFO - 2022-07-09 06:15:00 ~ 2022-07-09 23:45:00 - missing_time_splite
    47. 

  47. 2025-03-12 08:45:06,926 - data_handler.py - INFO - 缺失点数：69.0 - missing_time_splite
    48. 

  48. 2025-03-12 08:45:06,926 - data_handler.py - INFO - 缺失点数：69.0 - missing_time_splite
    49. 

  49. 2025-03-12 08:45:06,926 - data_handler.py - INFO - 2022-07-10 06:15:00 ~ 2022-07-10 23:45:00 - missing_time_splite
    50. 

  50. 2025-03-12 08:45:06,926 - data_handler.py - INFO - 2022-07-10 06:15:00 ~ 2022-07-10 23:45:00 - missing_time_splite
    51. 

  51. 2025-03-12 08:45:06,927 - data_handler.py - INFO - 缺失点数：69.0 - missing_time_splite
    52. 

  52. 2025-03-12 08:45:06,927 - data_handler.py - INFO - 缺失点数：69.0 - missing_time_splite
    53. 

  53. 2025-03-12 08:45:06,927 - data_handler.py - INFO - 2022-07-11 06:00:00 ~ 2022-07-11 23:45:00 - missing_time_splite
    54. 

  54. 2025-03-12 08:45:06,927 - data_handler.py - INFO - 2022-07-11 06:00:00 ~ 2022-07-11 23:45:00 - missing_time_splite
    55. 

  55. 2025-03-12 08:45:06,927 - data_handler.py - INFO - 缺失点数：70.0 - missing_time_splite
    56. 

  56. 2025-03-12 08:45:06,927 - data_handler.py - INFO - 缺失点数：70.0 - missing_time_splite
    57. 

  57. 2025-03-12 08:45:06,928 - data_handler.py - INFO - 2022-07-12 06:00:00 ~ 2022-07-12 23:45:00 - missing_time_splite
    58. 

  58. 2025-03-12 08:45:06,928 - data_handler.py - INFO - 2022-07-12 06:00:00 ~ 2022-07-12 23:45:00 - missing_time_splite
    59. 

  59. 2025-03-12 08:45:06,928 - data_handler.py - INFO - 缺失点数：70.0 - missing_time_splite
    60. 

  60. 2025-03-12 08:45:06,928 - data_handler.py - INFO - 缺失点数：70.0 - missing_time_splite
    61. 

  61. 2025-03-12 08:45:06,929 - data_handler.py - INFO - 2022-07-13 06:00:00 ~ 2022-07-13 23:45:00 - missing_time_splite
    62. 

  62. 2025-03-12 08:45:06,929 - data_handler.py - INFO - 2022-07-13 06:00:00 ~ 2022-07-13 23:45:00 - missing_time_splite
    63. 

  63. 2025-03-12 08:45:06,929 - data_handler.py - INFO - 缺失点数：70.0 - missing_time_splite
    64. 

  64. 2025-03-12 08:45:06,929 - data_handler.py - INFO - 缺失点数：70.0 - missing_time_splite
    65. 

  65. 2025-03-12 08:45:06,930 - data_handler.py - INFO - 2022-07-14 23:00:00 ~ 2022-07-14 23:45:00 - missing_time_splite
    66. 

  66. 2025-03-12 08:45:06,930 - data_handler.py - INFO - 2022-07-14 23:00:00 ~ 2022-07-14 23:45:00 - missing_time_splite
    67. 

  67. 2025-03-12 08:45:06,930 - data_handler.py - INFO - 缺失点数：2.0 - missing_time_splite
    68. 

  68. 2025-03-12 08:45:06,930 - data_handler.py - INFO - 缺失点数：2.0 - missing_time_splite
    69. 

  69. 2025-03-12 08:45:06,930 - data_handler.py - INFO - 需要补值的点数：2.0 - missing_time_splite
    70. 

  70. 2025-03-12 08:45:06,930 - data_handler.py - INFO - 需要补值的点数：2.0 - missing_time_splite
    71. 

  71. 2025-03-12 08:45:06,943 - data_handler.py - INFO - 2022-08-01 20:00:00 ~ 2022-08-01 23:45:00 - missing_time_splite
    72. 

  72. 2025-03-12 08:45:06,943 - data_handler.py - INFO - 2022-08-01 20:00:00 ~ 2022-08-01 23:45:00 - missing_time_splite
    73. 

  73. 2025-03-12 08:45:06,943 - data_handler.py - INFO - 缺失点数：14.0 - missing_time_splite
    74. 

  74. 2025-03-12 08:45:06,943 - data_handler.py - INFO - 缺失点数：14.0 - missing_time_splite
    75. 

  75. 2025-03-12 08:45:06,944 - data_handler.py - INFO - 2022-08-02 21:00:00 ~ 2022-08-02 23:45:00 - missing_time_splite
    76. 

  76. 2025-03-12 08:45:06,944 - data_handler.py - INFO - 2022-08-02 21:00:00 ~ 2022-08-02 23:45:00 - missing_time_splite
    77. 

  77. 2025-03-12 08:45:06,944 - data_handler.py - INFO - 缺失点数：10.0 - missing_time_splite
    78. 

  78. 2025-03-12 08:45:06,944 - data_handler.py - INFO - 缺失点数：10.0 - missing_time_splite
    79. 

  79. 2025-03-12 08:45:06,945 - data_handler.py - INFO - 2022-08-04 00:00:00 ~ 2022-08-04 23:15:00 - missing_time_splite
    80. 

  80. 2025-03-12 08:45:06,945 - data_handler.py - INFO - 2022-08-04 00:00:00 ~ 2022-08-04 23:15:00 - missing_time_splite
    81. 

  81. 2025-03-12 08:45:06,946 - data_handler.py - INFO - 缺失点数：92.0 - missing_time_splite
    82. 

  82. 2025-03-12 08:45:06,946 - data_handler.py - INFO - 缺失点数：92.0 - missing_time_splite
    83. 

  83. 2025-03-12 08:45:06,946 - data_handler.py - INFO - 2022-08-05 00:00:00 ~ 2022-08-05 23:15:00 - missing_time_splite
    84. 

  84. 2025-03-12 08:45:06,946 - data_handler.py - INFO - 2022-08-05 00:00:00 ~ 2022-08-05 23:15:00 - missing_time_splite
    85. 

  85. 2025-03-12 08:45:06,946 - data_handler.py - INFO - 缺失点数：92.0 - missing_time_splite
    86. 

  86. 2025-03-12 08:45:06,946 - data_handler.py - INFO - 缺失点数：92.0 - missing_time_splite
    87. 

  87. 2025-03-12 08:45:06,947 - data_handler.py - INFO - 2022-08-06 00:00:00 ~ 2022-08-06 23:15:00 - missing_time_splite
    88. 

  88. 2025-03-12 08:45:06,947 - data_handler.py - INFO - 2022-08-06 00:00:00 ~ 2022-08-06 23:15:00 - missing_time_splite
    89. 

  89. 2025-03-12 08:45:06,947 - data_handler.py - INFO - 缺失点数：92.0 - missing_time_splite
    90. 

  90. 2025-03-12 08:45:06,947 - data_handler.py - INFO - 缺失点数：92.0 - missing_time_splite
    91. 

  91. 2025-03-12 08:45:06,947 - data_handler.py - INFO - 2022-08-07 00:00:00 ~ 2022-08-07 23:15:00 - missing_time_splite
    92. 

  92. 2025-03-12 08:45:06,947 - data_handler.py - INFO - 2022-08-07 00:00:00 ~ 2022-08-07 23:15:00 - missing_time_splite
    93. 

  93. 2025-03-12 08:45:06,947 - data_handler.py - INFO - 缺失点数：92.0 - missing_time_splite
    94. 

  94. 2025-03-12 08:45:06,947 - data_handler.py - INFO - 缺失点数：92.0 - missing_time_splite
    95. 

  95. 2025-03-12 08:45:06,948 - data_handler.py - INFO - 数据总数：8207, 时序缺失的间隔：3, 其中，较长的时间间隔：14 - missing_time_splite
    96. 

  96. 2025-03-12 08:45:06,948 - data_handler.py - INFO - 数据总数：8207, 时序缺失的间隔：3, 其中，较长的时间间隔：14 - missing_time_splite
    97. 

  97. 2025-03-12 08:45:06,948 - data_handler.py - INFO - 需要补值的总点数：4.0 - missing_time_splite
    98. 

  98. 2025-03-12 08:45:06,948 - data_handler.py - INFO - 需要补值的总点数：4.0 - missing_time_splite
    99. 

  99. 2025-03-12 08:45:06,949 - data_handler.py - INFO - 再次测算，需要插值的总点数为：4.0 - fill_train_data
    100. 

  100. 2025-03-12 08:45:06,949 - data_handler.py - INFO - 再次测算，需要插值的总点数为：4.0 - fill_train_data
    101. 

  101. 2025-03-12 08:45:09,257 - data_handler.py - INFO - 特征处理-训练数据-不满足time_step - get_train_data
    102. 

  102. 2025-03-12 08:45:09,257 - data_handler.py - INFO - 特征处理-训练数据-不满足time_step - get_train_data
    103. 

  103. 2025-03-12 08:45:09,257 - data_handler.py - INFO - 特征处理-训练数据-无法进行最小分割 - get_train_data
    104. 

  104. 2025-03-12 08:45:09,257 - data_handler.py - INFO - 特征处理-训练数据-无法进行最小分割 - get_train_data
    105. 

  105. 2025-03-12 08:45:09,257 - data_handler.py - INFO - 特征处理-训练数据-不满足time_step - get_train_data
    106. 

  106. 2025-03-12 08:45:09,257 - data_handler.py - INFO - 特征处理-训练数据-不满足time_step - get_train_data
    107. 

  107. 2025-03-12 08:45:09,257 - data_handler.py - INFO - 特征处理-训练数据-无法进行最小分割 - get_train_data
    108. 

  108. 2025-03-12 08:45:09,257 - data_handler.py - INFO - 特征处理-训练数据-无法进行最小分割 - get_train_data
    109. 

  109. 2025-03-12 08:45:09,257 - data_handler.py - INFO - 特征处理-训练数据-不满足time_step - get_train_data
    110. 

  110. 2025-03-12 08:45:09,257 - data_handler.py - INFO - 特征处理-训练数据-不满足time_step - get_train_data
    111. 

  111. 2025-03-12 08:45:09,257 - data_handler.py - INFO - 特征处理-训练数据-无法进行最小分割 - get_train_data
    112. 

  112. 2025-03-12 08:45:09,257 - data_handler.py - INFO - 特征处理-训练数据-无法进行最小分割 - get_train_data
    113. 

  113. 2025-03-12 08:45:09,257 - data_handler.py - INFO - 特征处理-训练数据-不满足time_step - get_train_data
    114. 

  114. 2025-03-12 08:45:09,257 - data_handler.py - INFO - 特征处理-训练数据-不满足time_step - get_train_data
    115. 

  115. 2025-03-12 08:45:09,258 - data_handler.py - INFO - 特征处理-训练数据-无法进行最小分割 - get_train_data
    116. 

  116. 2025-03-12 08:45:09,258 - data_handler.py - INFO - 特征处理-训练数据-无法进行最小分割 - get_train_data
    117. 

  117. 2025-03-12 08:45:14,704 - tf_lstm.py - INFO - -----模型训练经过19轮迭代----- - training
    118. 

  118. 2025-03-12 08:45:14,704 - tf_lstm.py - INFO - -----模型训练经过19轮迭代----- - training
    119. 

  119. 2025-03-12 08:45:14,705 - tf_lstm.py - INFO - 训练集损失函数为：[0.63415 0.26059 0.22963 0.22361 0.21993 0.21915 0.2187  0.21845 0.21829
    120. 

  120.  0.21818 0.2181  0.21804 0.21799 0.21795 0.21791 0.21787 0.21784 0.21781
    121. 

  121.  0.21779] - training
    122. 

  122. 2025-03-12 08:45:14,705 - tf_lstm.py - INFO - 训练集损失函数为：[0.63415 0.26059 0.22963 0.22361 0.21993 0.21915 0.2187  0.21845 0.21829
    123. 

  123.  0.21818 0.2181  0.21804 0.21799 0.21795 0.21791 0.21787 0.21784 0.21781
    124. 

  124.  0.21779] - training
    125. 

  125. 2025-03-12 08:45:14,705 - tf_lstm.py - INFO - 验证集损失函数为：[0.3779  0.29081 0.26585 0.26052 0.25942 0.25902 0.25886 0.25881 0.2588
    126. 

  126.  0.25881 0.25883 0.25885 0.25888 0.2589  0.25892 0.25894 0.25896 0.25898
    127. 

  127.  0.259  ] - training
    128. 

  128. 2025-03-12 08:45:14,705 - tf_lstm.py - INFO - 验证集损失函数为：[0.3779  0.29081 0.26585 0.26052 0.25942 0.25902 0.25886 0.25881 0.2588
    129. 

  129.  0.25881 0.25883 0.25885 0.25888 0.2589  0.25892 0.25894 0.25896 0.25898
    130. 

  130.  0.259  ] - training
    131. 

  131. 2025-03-12 08:45:14,706 - saving_api.py - WARNING - The `save_format` argument is deprecated in Keras 3. We recommend removing this argument as it can be inferred from the file path. Received: save_format=h5 - save_model
    132. 

  132. 2025-03-12 08:45:14,706 - saving_api.py - WARNING - The `save_format` argument is deprecated in Keras 3. We recommend removing this argument as it can be inferred from the file path. Received: save_format=h5 - save_model
    133. 

  133. 2025-03-12 08:45:14,706 - saving_api.py - WARNING - You are saving your model as an HDF5 file via `model.save()` or `keras.saving.save_model(model)`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')` or `keras.saving.save_model(model, 'my_model.keras')`.  - save_model
    134. 

  134. 2025-03-12 08:45:14,706 - saving_api.py - WARNING - You are saving your model as an HDF5 file via `model.save()` or `keras.saving.save_model(model)`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')` or `keras.saving.save_model(model, 'my_model.keras')`.  - save_model
    135. 

  135. 2025-03-12 08:46:41,315 - logs.py - INFO - 日志输出路径为：E:\compete\app\logs - getLogName
    136. 

  136. 2025-03-12 08:47:43,615 - logs.py - INFO - 日志输出路径为：E:\compete\app\logs - getLogName
    137. 

  137. 2025-03-12 08:52:20,117 - logs.py - INFO - 日志输出路径为：E:\compete\app\logs - getLogName
    138. 

  138. 2025-03-12 08:52:20,294 - saving_utils.py - WARNING - Compiled the loaded model, but the compiled metrics have yet to be built. `model.compile_metrics` will be empty until you train or evaluate the model. - try_build_compiled_arguments
    139. 

  139. 2025-03-12 08:52:20,294 - saving_utils.py - WARNING - Compiled the loaded model, but the compiled metrics have yet to be built. `model.compile_metrics` will be empty until you train or evaluate the model. - try_build_compiled_arguments
    140. 

  140. 2025-03-12 08:52:20,595 - tf_lstm.py - INFO - 执行预测方法 - predict
    141. 

  141. 2025-03-12 08:52:20,595 - tf_lstm.py - INFO - 执行预测方法 - predict
    142. 

  142. 2025-03-12 08:52:52,426 - logs.py - INFO - 日志输出路径为：E:\compete\app\logs - getLogName
    143. 

  143. 2025-03-12 08:52:52,579 - saving_utils.py - WARNING - Compiled the loaded model, but the compiled metrics have yet to be built. `model.compile_metrics` will be empty until you train or evaluate the model. - try_build_compiled_arguments
    144. 

  144. 2025-03-12 08:52:52,579 - saving_utils.py - WARNING - Compiled the loaded model, but the compiled metrics have yet to be built. `model.compile_metrics` will be empty until you train or evaluate the model. - try_build_compiled_arguments
    145. 

  145. 2025-03-12 08:52:52,869 - tf_lstm.py - INFO - 执行预测方法 - predict
    146. 

  146. 2025-03-12 08:52:52,869 - tf_lstm.py - INFO - 执行预测方法 - predict
    147.