Refactor preprocessing logic for Chronos-2#493
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shchur
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Apr 28, 2026
abdulfatir
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Apr 30, 2026
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Thanks @shchur!
Overall, I don't have major concerns. The main thing we need to be fine with is that now there will be multiple validation paths which must be maintained. However, I think the major ones are list_of_dicts and dataframe, so it should not be too difficult to keep them tidy.
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Maybe list_of_dicts to align with the naming elsewhere?
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| - Unseen (item, category) pairs get the item mean as fallback (via smoothing formula) | ||
| - Completely unseen categories in future (cat_code=-1) get the item mean |
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What's the difference between these two?
| - future_id_codes (if provided) are valid item IDs that appear in id_codes | ||
| - future_cat_codes may contain -1 for unseen categories (encoded as NaN) | ||
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| Edge cases |
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What about NaNs in the categories (not in target)?
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Problem
Converting raw data to
list[PreparedInput]is extremely slow when categorical covariates are present. For M5 (30K items, 50M rows, 7 categorical columns), preprocessing takes ~8 minutes. The bottlenecks are:validate_df_inputsandvalidate_and_prepare_single_dict_inputProposed design
New module
chronos/chronos2/preprocess.pywith four entry points:from_tensor()from_tensor_list()from_dataframe()from_dict_list()Key changes:
_target_encode()uses bincount across all items at once instead of per-item sklearn callsBreaking changes
list[dict]inputs (all items must have same structure)Expected speedup
~20x faster on M5 dataset (8 min → ~25s based on earlier prototyping)
This PR contains only the design (signatures + docstrings). Implementation to follow.
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