def set_default_params(self,
accuracy=None, time_tolerance=None, interpretability=None,
**kwargs):
# First call the parent set_default_params
LightGBMModel.set_default_params(
self,
accuracy=accuracy,
time_tolerance=time_tolerance,
interpretability=interpretability,
**kwargs
)
# Then modify the parameters
self.params["grow_policy"] = "lossguide"
self.params["max_leaves"] = 8192
self.params["max_depth"] = -1
def set_default_params(self,
accuracy=None,
time_tolerance=None,
interpretability=None,
**kwargs):
# First call the LightGBM set_default_params
# This will input all model parameters just like DAI would do.
LightGBMModel.set_default_params(self,
accuracy=accuracy,
time_tolerance=time_tolerance,
interpretability=interpretability,
**kwargs)
# Now we just need to tell LightGBM to do quantile regression
self.params["objective"] = "quantile"
self.params["alpha"] = QuantileRegressionLightGBMModel._quantile
def set_default_params(self,
accuracy=None, time_tolerance=None, interpretability=None,
**kwargs):
# First call the LightGBM set_default_params
# This will input all model parameters just like DAI would do.
LightGBMModel.set_default_params(
self,
accuracy=accuracy,
time_tolerance=time_tolerance,
interpretability=interpretability,
**kwargs
)
# Now we just need to tell LightGBM to use tweedie distribution
self.params["objective"] = "tweedie"
self.params["tweedie_variance_power"] = TweedieLightGBMModel._tweedie_variance_power
def set_default_params(self,
accuracy=None,
time_tolerance=None,
interpretability=None,
**kwargs):
# Define the global loss
# global custom_asymmetric_objective
# First call the LightGBM set_default_params
# This will input all model parameters just like DAI would do.
LightGBMModel.set_default_params(self,
accuracy=accuracy,
time_tolerance=time_tolerance,
interpretability=interpretability,
**kwargs)
# Now we just need to tell LightGBM that it has to optimize for our custom objective
# And we are done
self.params["objective"] = custom_asymmetric_objective