def save(self, path):
"""
Save method allows users to save SmartPredictor object on disk using a pickle file.
Save method can be useful: you don't have to recompile to display results later.
Load_smartpredictor method allow to load your SmartPredictor object saved. (See example below)
Parameters
----------
path : str
File path to store the pickle file
Example
--------
>>> predictor.save('path_to_pkl/predictor.pkl')
>>> from shapash.utils.load_smartpredictor import load_smartpredictor
>>> predictor_load = load_smartpredictor('path_to_pkl/predictor.pkl')
"""
dict_to_save = {}
for att in self.__dict__.keys():
if (isinstance(getattr(self, att),
(list, dict, pd.DataFrame, pd.Series, type(None)))
or att == "model" or att == "explainer"
or att == "preprocessing") and not att == "data":
dict_to_save.update({att: getattr(self, att)})
save_pickle(dict_to_save, path)
def save(self, path):
"""
Save method allows user to save SmartExplainer object on disk
using a pickle file.
Save method can be useful: you don't have to recompile to display
results later
Parameters
----------
path : str
File path to store the pickle file
Example
--------
>>> xpl.save('path_to_pkl/xpl.pkl')
"""
dict_to_save = {}
for att in self.__dict__.keys():
if isinstance(getattr(self, att), (list, dict, pd.DataFrame, pd.Series, type(None), bool)) or att == "model":
dict_to_save.update({att: getattr(self, att)})
save_pickle(dict_to_save, path)