Add possibility of using multiple models

[montanarograziano]

Description of Problem:
Sometimes multiple models are trained
(on the same dataset, based on some criteria/conditions (e.g. dataset is too big to train a single model, or there is too much variability in the data). Currently, the SmartExplainer object accept a single model, meaning that multiple SmartExplainer objects must be created to address this problem and therefore multiple apps. Would this be a useful scenario of someone?

Overview of the Solution:
Add the possibility of passing a list of models instead of a single one to the SmartExplainer constructor and configure their usage based on a list of conditions.

Examples:
Something like this:

xpl = SmartExplainer(
    models=[model1, model2, model3],
    features_dict=features_dict,
    conditions=[df['avg'] >= 20, df['avg'] > (20 & df['avg'] <= 50), (df['avg'] > 50 & df['avg'] < 100)]
)

Blockers:

Definition of Done:

[ThomasBouche]

Hi,
Thanks for your issue,

This type of use is interesting. But development may not be straightforward.

Contributions can be compiled into a single webapp. several steps are required to compile models individually, then aggregate them.
You have to do something like this:

Compute your contributions

xpl1 = SmartExplainer(models=model1)
xpl1.compile(Xtest1)

xpl2 = SmartExplainer(models=model2)
xpl2.compile(Xtest2)

Then concat your contributions

#for regression
df_contributions = pd.concat([xpl1.contributions, xpl2.contributions],axis=0)
#for classification, you have to concat each class
df_contributions = [pd.concat([xpl1.contributions[0], xpl2.contributions[0]],axis=0),
    pd.concat([xpl1.contributions[1], xpl2.contributions[1]],axis=0)]

And give this contributions to a new shapash, if you give your contribution, you can give any model accept by shapash.
You can also give an additionnal dataset with your feature 'avg' to filter in the webapp

xpl_all = SmartExplainer(models=model1)
xpl_all.compile(
 Xtest_all,
 contributions = df_contributions,
 additional_data=X_additional
)

I hope this solution works until there is a more appropriate development for this type of use.

[montanarograziano]

Thank you for your response, this is exactly the solution I adopted temporarily for my use case.