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Export: 40 per-store boosters, WRMSSE=145.56

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Files changed (50) hide show
  1. README.md +47 -0
  2. SHA256SUMS +50 -0
  3. categorical_maps.json +3084 -0
  4. feature_schema.json +88 -0
  5. history_tail.parquet +3 -0
  6. models/model_store=CA_1_hblock=block_15_21.txt +0 -0
  7. models/model_store=CA_1_hblock=block_1_7.txt +0 -0
  8. models/model_store=CA_1_hblock=block_22_28.txt +0 -0
  9. models/model_store=CA_1_hblock=block_8_14.txt +0 -0
  10. models/model_store=CA_2_hblock=block_15_21.txt +0 -0
  11. models/model_store=CA_2_hblock=block_1_7.txt +0 -0
  12. models/model_store=CA_2_hblock=block_22_28.txt +0 -0
  13. models/model_store=CA_2_hblock=block_8_14.txt +0 -0
  14. models/model_store=CA_3_hblock=block_15_21.txt +0 -0
  15. models/model_store=CA_3_hblock=block_1_7.txt +0 -0
  16. models/model_store=CA_3_hblock=block_22_28.txt +0 -0
  17. models/model_store=CA_3_hblock=block_8_14.txt +0 -0
  18. models/model_store=CA_4_hblock=block_15_21.txt +0 -0
  19. models/model_store=CA_4_hblock=block_1_7.txt +0 -0
  20. models/model_store=CA_4_hblock=block_22_28.txt +0 -0
  21. models/model_store=CA_4_hblock=block_8_14.txt +0 -0
  22. models/model_store=TX_1_hblock=block_15_21.txt +0 -0
  23. models/model_store=TX_1_hblock=block_1_7.txt +0 -0
  24. models/model_store=TX_1_hblock=block_22_28.txt +0 -0
  25. models/model_store=TX_1_hblock=block_8_14.txt +0 -0
  26. models/model_store=TX_2_hblock=block_15_21.txt +0 -0
  27. models/model_store=TX_2_hblock=block_1_7.txt +0 -0
  28. models/model_store=TX_2_hblock=block_22_28.txt +0 -0
  29. models/model_store=TX_2_hblock=block_8_14.txt +0 -0
  30. models/model_store=TX_3_hblock=block_15_21.txt +0 -0
  31. models/model_store=TX_3_hblock=block_1_7.txt +0 -0
  32. models/model_store=TX_3_hblock=block_22_28.txt +0 -0
  33. models/model_store=TX_3_hblock=block_8_14.txt +0 -0
  34. models/model_store=WI_1_hblock=block_15_21.txt +0 -0
  35. models/model_store=WI_1_hblock=block_1_7.txt +0 -0
  36. models/model_store=WI_1_hblock=block_22_28.txt +0 -0
  37. models/model_store=WI_1_hblock=block_8_14.txt +0 -0
  38. models/model_store=WI_2_hblock=block_15_21.txt +0 -0
  39. models/model_store=WI_2_hblock=block_1_7.txt +0 -0
  40. models/model_store=WI_2_hblock=block_22_28.txt +0 -0
  41. models/model_store=WI_2_hblock=block_8_14.txt +0 -0
  42. models/model_store=WI_3_hblock=block_15_21.txt +0 -0
  43. models/model_store=WI_3_hblock=block_1_7.txt +0 -0
  44. models/model_store=WI_3_hblock=block_22_28.txt +0 -0
  45. models/model_store=WI_3_hblock=block_8_14.txt +0 -0
  46. parity_predictions.parquet +3 -0
  47. predictions.parquet +3 -0
  48. predictions_meta.json +60 -0
  49. submission.csv +0 -0
  50. training_config.json +79 -0
README.md ADDED
@@ -0,0 +1,47 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ # M5 Demand Forecasting Artifacts
2
+
3
+ ## What the models predict
4
+ The models predict weekly demand (sales) for 28-day horizons (d_1942 through d_1969) for 30,490 M5 series.
5
+
6
+ ## Exact input contract
7
+ - Features must be computed as-of origin day d_1913 using only data with day index <= D
8
+ - Categorical features must be encoded using categorical_maps.json with .map()
9
+ - Feature order must match feature_schema.json feature_names element by element
10
+ - All float columns must be float32; no float64 or object columns permitted
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+ - Target encoding (dept_id, store_id, item_id) is computed only on data before the origin (leakage-safe)
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+
13
+ ## Files required together
14
+ Boosters alone are insufficient. Required files:
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+ - artifacts/feature_schema.json
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+ - artifacts/categorical_maps.json
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+ - artifacts/training_config.json
18
+ - data/processed/m5_melted.parquet
19
+
20
+ ## Known limitation
21
+ Predictions valid only for recorded forecast origin unless history is supplied
22
+
23
+ ## Recorded local WRMSSE per fold
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+ - Fold A: 148.327863
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+ - Fold B: 121.276794
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+ - Fold C: 167.073294
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+ - Mean: 145.559317
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+
29
+ ## Copy-pasteable load instructions
30
+ ```python
31
+ import lightgbm as lgb
32
+ import pandas as pd
33
+
34
+ models = {}
35
+ for f in os.listdir('artifacts/models/'):
36
+ model = lgb.Booster(model_file='artifacts/models/' + f)
37
+ models[f] = model
38
+
39
+ with open('artifacts/training_config.json') as f:
40
+ config = json.load(f)
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+ with open('artifacts/feature_schema.json') as f:
42
+ schema = json.load(f)
43
+ with open('artifacts/categorical_maps.json') as f:
44
+ cat_maps = json.load(f)
45
+
46
+ df = pd.read_parquet('data/processed/m5_melted.parquet')
47
+ ```
SHA256SUMS ADDED
@@ -0,0 +1,50 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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+ 9a7c0ac118919cb8087194cea2638e60063dc8e181a26aa89272d3dd4724815f SHA256SUMS
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+ 550779bc6804a34668d6db43dd7b57df23a3bc465b456874437d03940a77439b categorical_maps.json
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+ fa03349beb59db7282e6557c6d24f74fcfe6429537fcbeb024fd041bbe16b633 feature_schema.json
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+ a4a50978a69487c1e8af483e046492a768decb2e942fa34e3887ed1cccd50de3 history_tail.parquet
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