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Online predictions with LIME (or how to pickle an Explainer object?)

See original GitHub issue

Hi,

I’m considering deploying LIME in a production environment; say, the user inputs the relevant columns and some server is listening and using LIME to return LIME explanations.

I’m not sure how to go about this, but I thought the best way would be to somehow pickle the Explainer object, that then could be read on demand and then feed it with the user input. Would something like that work? Not sure if it would still require LIME and sci-kit learn to be installed and loaded each time.

Issue Analytics

State:
Created 6 years ago
Comments:9 (5 by maintainers)

Top GitHub Comments

3reactions

marcotcrcommented, Jan 19, 2018

No, no reason. I think the only place we store lambda functions is here, and they could easily be removed from there.

1reaction

gregrosscommented, Apr 11, 2019

Dill is working well, for me in the context of the OP’s question.

To save:

import dill
with open(explainer_filename, 'wb') as f: dill.dump(explainer, f)

To load: with open(explainer_filename, 'rb') as f: explainer = dill.load(f)

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