File size: 1,570 Bytes
85868e0 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 | # M5 Demand Forecasting Artifacts
## What the models predict
The models predict weekly demand (sales) for 28-day horizons (d_1942 through d_1969) for 30,490 M5 series.
## Exact input contract
- Features must be computed as-of origin day d_1913 using only data with day index <= D
- Categorical features must be encoded using categorical_maps.json with .map()
- Feature order must match feature_schema.json feature_names element by element
- All float columns must be float32; no float64 or object columns permitted
- Target encoding (dept_id, store_id, item_id) is computed only on data before the origin (leakage-safe)
## Files required together
Boosters alone are insufficient. Required files:
- artifacts/feature_schema.json
- artifacts/categorical_maps.json
- artifacts/training_config.json
- data/processed/m5_melted.parquet
## Known limitation
Predictions valid only for recorded forecast origin unless history is supplied
## Recorded local WRMSSE per fold
- Fold A: 148.327863
- Fold B: 121.276794
- Fold C: 167.073294
- Mean: 145.559317
## Copy-pasteable load instructions
```python
import lightgbm as lgb
import pandas as pd
models = {}
for f in os.listdir('artifacts/models/'):
model = lgb.Booster(model_file='artifacts/models/' + f)
models[f] = model
with open('artifacts/training_config.json') as f:
config = json.load(f)
with open('artifacts/feature_schema.json') as f:
schema = json.load(f)
with open('artifacts/categorical_maps.json') as f:
cat_maps = json.load(f)
df = pd.read_parquet('data/processed/m5_melted.parquet')
```
|