def _load(properties):
"""Load a LGBMExplainableModel from the given properties.
:param properties: A serialized dictionary representation of the LGBMExplainableModel.
:type properties: dict
:return: The deserialized LGBMExplainableModel.
:rtype: azureml.explain.model.mimic.models.LGBMExplainableModel
"""
# create the LGBMExplainableModel without any properties using the __new__ function, similar to pickle
lightgbm = LGBMExplainableModel.__new__(LGBMExplainableModel)
# Get _n_features
_n_features = properties.pop(_N_FEATURES)
# If classification case get _n_classes
if json.loads(properties[LightGBMSerializationConstants.MULTICLASS]):
_n_classes = properties.pop(_N_CLASSES)
# load all of the properties
for key, value in properties.items():
# Regenerate the properties on the fly
if key in LightGBMSerializationConstants.nonify_properties:
if key == LightGBMSerializationConstants.LOGGER:
parent = logging.getLogger(__name__)
lightgbm_identity = json.loads(properties[LightGBMSerializationConstants.IDENTITY])
lightgbm.__dict__[key] = parent.getChild(lightgbm_identity)
elif key == LightGBMSerializationConstants.TREE_EXPLAINER:
lightgbm.__dict__[key] = None
else:
raise Exception("Unknown nonify key on deserialize in LightGBMExplainableModel: {}".format(key))
elif key in LightGBMSerializationConstants.save_properties:
# Load the booster from file and re-create the LGBMClassifier or LGBMRegressor
# This is not recommended but can be necessary to get around pickle being not secure
# See here for more info:
# https://github.com/Microsoft/LightGBM/issues/1942
# https://github.com/Microsoft/LightGBM/issues/1217
if json.loads(properties[LightGBMSerializationConstants.MULTICLASS]):
new_lgbm = LGBMClassifier()
lgbm_booster = Booster(params={LightGBMSerializationConstants.MODEL_STR: value})
new_lgbm._Booster = lgbm_booster
new_lgbm._n_classes = _n_classes
else:
new_lgbm = LGBMRegressor()
lgbm_booster = Booster(params={LightGBMSerializationConstants.MODEL_STR: value})
new_lgbm._Booster = lgbm_booster
new_lgbm._n_features = _n_features
lightgbm.__dict__[key] = new_lgbm
elif key in LightGBMSerializationConstants.enum_properties:
# NOTE: If more enums added in future, will need to handle this differently
lightgbm.__dict__[key] = ShapValuesOutput(json.loads(value))
else:
lightgbm.__dict__[key] = json.loads(value)
return lightgbm
def _load(properties):
"""Load a LGBMExplainableModel from the given properties.
:param properties: A serialized dictionary representation of the LGBMExplainableModel.
:type properties: dict
:return: The deserialized LGBMExplainableModel.
:rtype: interpret_community.mimic.models.LGBMExplainableModel
"""
# create the LGBMExplainableModel without any properties using the __new__ function, similar to pickle
lgbm_model = LGBMExplainableModel.__new__(LGBMExplainableModel)
# Get _n_features
_n_features = properties.pop(_N_FEATURES)
# If classification case get _n_classes
if json.loads(properties[LightGBMSerializationConstants.MULTICLASS]):
_n_classes = properties.pop(_N_CLASSES)
fitted_ = None
if _FITTED in properties:
fitted_ = json.loads(properties[_FITTED])
elif version.parse('3.3.1') <= version.parse(lightgbm.__version__):
# If deserializing older model in newer version set this to true to prevent errors on calls
fitted_ = True
# load all of the properties
for key, value in properties.items():
# Regenerate the properties on the fly
if key in LightGBMSerializationConstants.nonify_properties:
if key == LightGBMSerializationConstants.LOGGER:
parent = logging.getLogger(__name__)
lightgbm_identity = json.loads(
properties[LightGBMSerializationConstants.IDENTITY])
lgbm_model.__dict__[key] = parent.getChild(
lightgbm_identity)
elif key == LightGBMSerializationConstants.TREE_EXPLAINER:
lgbm_model.__dict__[key] = None
else:
raise Exception(
"Unknown nonify key on deserialize in LightGBMExplainableModel: {}"
.format(key))
elif key in LightGBMSerializationConstants.save_properties:
# Load the booster from file and re-create the LGBMClassifier or LGBMRegressor
# This is not recommended but can be necessary to get around pickle being not secure
# See here for more info:
# https://github.com/Microsoft/LightGBM/issues/1942
# https://github.com/Microsoft/LightGBM/issues/1217
booster_args = {
LightGBMSerializationConstants.MODEL_STR: value
}
is_multiclass = json.loads(
properties[LightGBMSerializationConstants.MULTICLASS])
if is_multiclass:
objective = LightGBMSerializationConstants.MULTICLASS
else:
objective = LightGBMSerializationConstants.REGRESSION
if LightGBMSerializationConstants.MODEL_STR in inspect.getargspec(
Booster).args:
extras = {
LightGBMSerializationConstants.OBJECTIVE: objective
}
lgbm_booster = Booster(**booster_args, params=extras)
else:
# For backwards compatibility with older versions of lightgbm
booster_args[
LightGBMSerializationConstants.OBJECTIVE] = objective
lgbm_booster = Booster(params=booster_args)
if is_multiclass:
new_lgbm = LGBMClassifier()
new_lgbm._Booster = lgbm_booster
new_lgbm._n_classes = _n_classes
else:
new_lgbm = LGBMRegressor()
new_lgbm._Booster = lgbm_booster
# Specify fitted_ for newer versions of lightgbm on deserialize
if fitted_ is not None:
new_lgbm.fitted_ = fitted_
new_lgbm._n_features = _n_features
lgbm_model.__dict__[key] = new_lgbm
elif key in LightGBMSerializationConstants.enum_properties:
# NOTE: If more enums added in future, will need to handle this differently
lgbm_model.__dict__[key] = ShapValuesOutput(json.loads(value))
else:
lgbm_model.__dict__[key] = json.loads(value)
return lgbm_model
