def set_default_params(self,
accuracy=None,
time_tolerance=None,
interpretability=None,
**kwargs):
# First call the XGBoostGBMModel set_default_params
# This will input all model parameters just like DAI would do.
XGBoostGBMModel.set_default_params(self,
accuracy=accuracy,
time_tolerance=time_tolerance,
interpretability=interpretability,
**kwargs)
# Now we just need to tell XGBoost that it has to optimize for our custom objective
# And we are done
self.params["objective"] = custom_asymmetric_objective
def mutate_params(self,
get_best=False,
time_tolerance=None,
accuracy=None,
interpretability=None,
imbalance_ratio=None,
train_shape=None,
ncol_effective=None,
time_series=False,
ensemble_level=None,
score_f_name: str = None,
**kwargs):
# If we don't override the parent mutate_params method, DAI would have the opportunity
# to modify the objective and select the winner
# For demonstration purposes we purposely make sure that the objective
# is the one we want
# So first call the parent method to mutate parameters
params = XGBoostGBMModel.mutate_params(
self,
get_best=get_best,
time_tolerance=time_tolerance,
accuracy=accuracy,
interpretability=interpretability,
imbalance_ratio=imbalance_ratio,
train_shape=train_shape,
ncol_effective=ncol_effective,
time_series=time_series,
ensemble_level=ensemble_level,
score_f_name=score_f_name,
**kwargs)
# Now set the objective that DAI could have mutated
params["objective"] = custom_asymmetric_objective