def load(self, path):
"""
Load method allows Shapash user to use pickled SmartExplainer.
To use this method you must first declare your SmartExplainer object
Watch the following example
Parameters
----------
path : str
File path of the pickle file.
Example
--------
>>> xpl = SmartExplainer()
>>> xpl.load('path_to_pkl/xpl.pkl')
"""
dict_to_load = load_pickle(path)
if isinstance(dict_to_load, dict):
for elem in dict_to_load.keys():
setattr(self, elem, dict_to_load[elem])
self._case, self._classes = self.check_model()
self.state = self.choose_state(self.contributions)
else:
raise ValueError(
"pickle file must contain dictionary"
)
def load_smartpredictor(path):
"""
load_smartpredictor allows Shapash users to load SmartPredictor Object already saved into a pickle.
Parameters
----------
path : str
File path of the pickle file.
Example
--------
>>> predictor = load_smartpredictor('path_to_pkl/predictor.pkl')
"""
dict_to_load = load_pickle(path)
if isinstance(dict_to_load, dict):
predictor = SmartPredictor(
features_dict=dict_to_load['features_dict'],
model=dict_to_load['model'],
columns_dict=dict_to_load['columns_dict'],
explainer=dict_to_load['explainer'],
features_types=dict_to_load['features_types'],
label_dict=dict_to_load['label_dict'],
preprocessing=dict_to_load['preprocessing'],
postprocessing=dict_to_load['postprocessing'])
return predictor
else:
raise ValueError("pickle file must contain dictionary")