Power-law histogram generations #1165
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brilee
commented
Feb 5, 2024
lilac/data/dataset.py
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| # Defined for numeric features. | ||
| min_val: Optional[Union[float, date, datetime]] = None | ||
| max_val: Optional[Union[float, date, datetime]] = None | ||
| value_samples: Optional[list[float]] = None # Used for approximating histogram bins |
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This is a list of 100 floats; no need to transmit this to the client but I was too lazy to figure out how to nullify this when serializing the field
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you can nullify in get_stats in router_dataset.py right before returning the result, but no big deal
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oh, figured out how to exclude in pydantic
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tested some larger datasets for performance |
dsmilkov
approved these changes
Feb 5, 2024
lilac/data/dataset.py
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| # Defined for numeric features. | ||
| min_val: Optional[Union[float, date, datetime]] = None | ||
| max_val: Optional[Union[float, date, datetime]] = None | ||
| value_samples: Optional[list[float]] = None # Used for approximating histogram bins |
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you can nullify in get_stats in router_dataset.py right before returning the result, but no big deal
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Fixes #1161 partially. (by generating better distributions). If there are 95% NaN, then this still causes lopsided bars that don't represent value ranges properly.