Conformal prediction in MAPIE using existing models (e.g. https://mapie.readthedocs.io/en/latest/examples_regression/1-quickstart/plot_prefit.html#sphx-glr-examples-regression-1-quickstart-plot-prefit-py) supports models which have fit and predict attributes only. However, LGBM Regression models saved to disk, either using Booster method or pickle, don't have a "fit" attribute but a "refit" attribute (https://lightgbm.readthedocs.io/en/latest/pythonapi/lightgbm.Booster.html#lightgbm.Booster.refit). Unfortunately, MAPIE seems unable to use the refit method. Therefore, at this point, an LGBM Booster instance seems to be incompatible with MAPIE.
Enabling compatibility between LGBM Booster and MAPIE could be very useful for instances where a model is reused multiple times for prediction in the future and fitting a model from scratch is not possible.
Conformal prediction in MAPIE using existing models (e.g. https://mapie.readthedocs.io/en/latest/examples_regression/1-quickstart/plot_prefit.html#sphx-glr-examples-regression-1-quickstart-plot-prefit-py) supports models which have fit and predict attributes only. However, LGBM Regression models saved to disk, either using Booster method or pickle, don't have a "fit" attribute but a "refit" attribute (https://lightgbm.readthedocs.io/en/latest/pythonapi/lightgbm.Booster.html#lightgbm.Booster.refit). Unfortunately, MAPIE seems unable to use the refit method. Therefore, at this point, an LGBM Booster instance seems to be incompatible with MAPIE.
Enabling compatibility between LGBM Booster and MAPIE could be very useful for instances where a model is reused multiple times for prediction in the future and fitting a model from scratch is not possible.