Provide a parameter in order to decide if `None`/`np.nan` should be considered missing value or normal value

python-qds / qdscreen

Quasi-determinism screening for fast Bayesian Network Structure Learning (from T.Rahier's PhD thesis, 2018)

https://python-qds.github.io/qdscreen/

BSD 3-Clause "New" or "Revised" License

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Open smarie opened 1 year ago

smarie commented 1 year ago

Two different concepts:

A missing value should not be used in calculations of entropy or conditional entropies, and should be only used as "last resort" in the feature selection model (Series.mode have a flag to ignore nans)
a "none" level, if considered an acceptable value, should be used in entropy calculations and in the calculation of mode in the feature selection model.

smarie commented 1 year ago

Maybe we just need two parameters: "ignore_missing_values=True" and "missing_value=np.nan". The second can be changed for example to "N/A"