Missing data

[PARODBE]

Hi,

One question, the library have any option for missing data computation like bnlearn of R?

Thanks!

[erdogant]

There are no imputation functions for missing data. But if you create a function that does it (without a lot of dependencies of other packages), feel free to push it!

[harrietmwwright]

Im also looking for this functionality.
At the moment if you are trying to make a prediction on a dataset and remove one of the variables it will make the prediction, however, it will error if you provide the variable with a value of NaN. Is this doing some sort of imputation/estimation in the backend?

[PARODBE]

we can use bayes theorem and with the computed posteriors removed from the equation missing data?

[erdogant]

Can you maybe make a small example to demonstrate this? Maybe with the sprinkler data set?

[PARODBE]

I know that pymc3 library do this...I have read It in a hierarchical linear regression using bayesian approach, in this moment I don't remember the article, but this blog shows something like that: http://stronginference.com/missing-data-imputation.html

[erdogant]

Impute functionality implemented in case of missing values.
See docs over here.

Update to the latest version with:

pip install -U bnlearn

[PARODBE]

You could include the MICE approach but using the same bayesian model, like using MICE random forest but with this approach, so iteratively you use the input value without missing data to compute the missing data, but with a bayesian model. What do you think?

[erdogant]

This one? https://scikit-learn.org/1.5/modules/generated/sklearn.impute.IterativeImputer.html#sklearn.impute.IterativeImputer

[PARODBE]

More or less, it's an adaptation from the original. But the problem, if I'm not wrong It only supports quantitative data. This, also, there are other options which support categorical data, like this one: https://github.com/AnotherSamWilson/miceforest

But I'm not sure if you can include a bayesian model. I think that if you have build your TAN, FAN or whatever bayesian approach with your library, iteratively you build these models without missing data and predict missing data.

[erdogant]

The MICE functionality has been thanks to contributing of @Ananyapam7.
See here for more information.

Update to the latest version with:

pip install -U bnlearn