[BUG] Many Feature-extraction improvement

[MiguelGuima]

Good morning.

In an investigation project, we are developing an approach to predict the error of future models in an streaming data scenario, in wich data is allways coming and we need to decide when to retrain a model to not become obsolete.

For that purpose, we are extracting as many features we can from pymfe package (thanks so much for your work and continue doing such a beautifull work), and we come with an idea to use as many datasets we also can for the meta-feature extraction in order to maybe predict the error of any kind of dataset, instead of only one dataset containing only one problem/domain.

So, in the present work we are investigating this possibility with regression models for now, but we are asking for help in the sense that we would like to extract as many meta-features we can (we are aware of dimensionality problem and we apply strategies latter) and ignore the ones that can't be extracted like Fig.a, in some automated way instead of selecting some specific meta-features or groups, if that would be possible.

For example to avoid the Fig.b error in the extraction of the appended dataset, with mfe = MFE(groups="all", summary="all")

Fig.a

Fig.b

Dataset
2019.zip

Thanks again for your excelent work!

[FelSiq]

Hi @MiguelGuima,

It seems your data consists of regression problems, correct? If so, the pymfe does not currently support regression tasks, only classification problems, so it is expected bugs in the meta-feature extraction process.

Alternatives could be to either 1) discretize the dependent attribute y manually before fitting into the pymfe extractor, or 2) use the beta version of ts-pymfe which is intended for time-series and may also work with data streams.

Possibly related issue: #98.

[MiguelGuima]

Thanks for the quick response!

I didn't see that pymfe don't work with regression problems...

I am sure I will find a way around :)

Thanks again