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Mochinov

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Mochinov
Mar 15th, 2023
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  1.  
  2.  
  3. Метод _get_actual_shipments_df:
  4. key date actual
  5. 0 sku1_117 2022-04-01 1.0
  6. 1 sku2_117 2022-04-01 1.0
  7. 2 sku3_117 2022-04-01 1.0
  8. 3 sku4_117 2022-04-01 1.0
  9. 4 sku5_117 2022-04-01 1.0
  10. 5 sku6_117 2022-04-01 1.0
  11. 6 sku1_117 2022-04-02 2.0
  12. 7 sku2_117 2022-04-02 2.0
  13. 8 sku3_117 2022-04-02 2.0
  14. 9 sku4_117 2022-04-02 2.0
  15. 10 sku5_117 2022-04-02 2.0
  16. 11 sku6_117 2022-04-02 2.0
  17. 12 sku1_117 2022-04-03 3.0
  18. 13 sku2_117 2022-04-03 3.0
  19. 14 sku3_117 2022-04-03 3.0
  20. 15 sku4_117 2022-04-03 3.0
  21. 16 sku5_117 2022-04-03 3.0
  22. 17 sku6_117 2022-04-03 3.0
  23. 18 sku1_117 2022-04-04 4.0
  24. 19 sku2_117 2022-04-04 4.0
  25.  
  26.  
  27.  
  28. ---------- End _get_actual_shipments_df ---------
  29.  
  30. [2023-03-15 17:50:25,964: WARNING/ForkPoolWorker-8] /usr/src/app/forecasts/services/ml_interaction/ml_server_data_sender.py:261: FutureWarning: In a future version of pandas, a length 1 tuple will be returned when iterating over a groupby with a grouper equal to a list of length 1. Don't supply a list with a single grouper to avoid this warning.
  31. for _, group in grouped:
  32. [2023-03-15 17:50:26,011: WARNING/ForkPoolWorker-8]
  33. Метод _remove_innovations_from_df:
  34.  
  35. system_keys:
  36. {'sku3_117', 'sku1_117', 'sku2_117', 'sku5_117', 'sku6_117', 'sku4_117'}
  37.  
  38. elements_with_innovation_status:
  39. <CTEQuerySet []>
  40.  
  41. filtered_df:
  42. key date target partition product_key
  43. 0 sku1_117 2022-04-01 1.0 train sku1_117
  44. 1 sku1_117 2022-04-02 2.0 train sku1_117
  45. 2 sku1_117 2022-04-03 3.0 train sku1_117
  46. 3 sku1_117 2022-04-04 4.0 train sku1_117
  47. 4 sku1_117 2022-04-05 0.0 fc sku1_117
  48. ... ... ... ... ... ...
  49. 2205 sku6_117 2023-03-30 0.0 fc sku6_117
  50. 2206 sku6_117 2023-03-31 0.0 fc sku6_117
  51. 2207 sku6_117 2023-04-01 0.0 fc sku6_117
  52. 2208 sku6_117 2023-04-02 0.0 fc sku6_117
  53. 2209 sku6_117 2023-04-03 0.0 fc sku6_117
  54. [2210 rows x 5 columns]
  55.  
  56. ---------- End _remove_innovations_from_df ---------
  57.  
  58. [2023-03-15 17:50:26,029: WARNING/ForkPoolWorker-8]
  59.  
  60. Метод _collect_data:
  61.  
  62.  
  63. actual_shipments:
  64. key date actual
  65. 0 sku1_117 2022-04-01 1.0
  66. 6 sku1_117 2022-04-02 2.0
  67. 12 sku1_117 2022-04-03 3.0
  68. 18 sku1_117 2022-04-04 4.0
  69. 1 sku2_117 2022-04-01 1.0
  70. 7 sku2_117 2022-04-02 2.0
  71. 13 sku2_117 2022-04-03 3.0
  72. 19 sku2_117 2022-04-04 4.0
  73. 2 sku3_117 2022-04-01 1.0
  74. 8 sku3_117 2022-04-02 2.0
  75. 14 sku3_117 2022-04-03 3.0
  76. 3 sku4_117 2022-04-01 1.0
  77. 9 sku4_117 2022-04-02 2.0
  78. 15 sku4_117 2022-04-03 3.0
  79. 4 sku5_117 2022-04-01 1.0
  80. 10 sku5_117 2022-04-02 2.0
  81. 16 sku5_117 2022-04-03 3.0
  82. 5 sku6_117 2022-04-01 1.0
  83. 11 sku6_117 2022-04-02 2.0
  84. 17 sku6_117 2022-04-03 3.0
  85.  
  86. resulting_df:
  87. key date target partition product_key
  88. 0 sku1_117 2022-04-01 1.0 train sku1_117
  89. 1 sku1_117 2022-04-02 2.0 train sku1_117
  90. 2 sku1_117 2022-04-03 3.0 train sku1_117
  91. 3 sku1_117 2022-04-04 4.0 train sku1_117
  92. 4 sku1_117 2022-04-05 0.0 fc sku1_117
  93. ... ... ... ... ... ...
  94. 2205 sku6_117 2023-03-30 0.0 fc sku6_117
  95. 2206 sku6_117 2023-03-31 0.0 fc sku6_117
  96. 2207 sku6_117 2023-04-01 0.0 fc sku6_117
  97. 2208 sku6_117 2023-04-02 0.0 fc sku6_117
  98. 2209 sku6_117 2023-04-03 0.0 fc sku6_117
  99. [2210 rows x 5 columns]
  100.  
  101.  
  102. without_innovations:
  103. key date target partition product_key
  104. 0 sku1_117 2022-04-01 1.0 train sku1_117
  105. 1 sku1_117 2022-04-02 2.0 train sku1_117
  106. 2 sku1_117 2022-04-03 3.0 train sku1_117
  107. 3 sku1_117 2022-04-04 4.0 train sku1_117
  108. 4 sku1_117 2022-04-05 0.0 fc sku1_117
  109. ... ... ... ... ... ...
  110. 2205 sku6_117 2023-03-30 0.0 fc sku6_117
  111. 2206 sku6_117 2023-03-31 0.0 fc sku6_117
  112. 2207 sku6_117 2023-04-01 0.0 fc sku6_117
  113. 2208 sku6_117 2023-04-02 0.0 fc sku6_117
  114. 2209 sku6_117 2023-04-03 0.0 fc sku6_117
  115. [2210 rows x 5 columns]
  116.  
  117. ---------- End _collect_data ---------
  118.  
  119.  
  120. Метод _get_actual_shipments_df:
  121. key date actual
  122. 0 sku1_117 2022-04-01 1.0
  123. 1 sku2_117 2022-04-01 1.0
  124. 2 sku3_117 2022-04-01 1.0
  125. 3 sku4_117 2022-04-01 1.0
  126. 4 sku5_117 2022-04-01 1.0
  127. 5 sku6_117 2022-04-01 1.0
  128. 6 sku1_117 2022-04-02 2.0
  129. 7 sku2_117 2022-04-02 2.0
  130. 8 sku3_117 2022-04-02 2.0
  131. 9 sku4_117 2022-04-02 2.0
  132. 10 sku5_117 2022-04-02 2.0
  133. 11 sku6_117 2022-04-02 2.0
  134. 12 sku1_117 2022-04-03 3.0
  135. 13 sku2_117 2022-04-03 3.0
  136. 14 sku3_117 2022-04-03 3.0
  137. 15 sku4_117 2022-04-03 3.0
  138. 16 sku5_117 2022-04-03 3.0
  139. 17 sku6_117 2022-04-03 3.0
  140. 18 sku1_117 2022-04-04 4.0
  141. 19 sku2_117 2022-04-04 4.0
  142.  
  143.  
  144.  
  145. ---------- End _get_actual_shipments_df ---------
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