API tools faq
paste
Advertisement
Mochinov

Untitled

Mochinov
Mar 15th, 2023
214
0
Never
Add comment
Not a member of Pastebin yet? Sign Up, it unlocks many cool features!
text 5.80 KB | None | 0 0
raw download clone embed print report
  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 ---------
Advertisement
Add Comment
Please, Sign In to add comment
Advertisement
Public Pastes
Advertisement
We use cookies for various purposes including analytics. By continuing to use Pastebin, you agree to our use of cookies as described in the Cookies Policy.  OK, I Understand
Not a member of Pastebin yet?
Sign Up, it unlocks many cool features!