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"""Modified version of Driverless AI's internal XGBoost implementation with a custom objective function (used for tree split finding).
from h2oaicore.models import BaseCustomModel, XGBoostGBMModel
import numpy as np
# The custom objective function will be pickled along with the underlying XGBoost model for persistance purposes
# as a result it can't a lambda function or a method of the custom model object
# The only option is to make the function global in the following manner
def custom_asymmetric_objective(y_true, y_pred):
"""Asymetric MSE loss
A custom loss has to return the gradient and the hessian of the loss or objective function
This is not an evaluation but the loss LightGBM will optimize during training
residual = (y_true - y_pred).astype("float")
grad = np.where(residual < 0, -2 * 10.0 * residual, -2 * residual)
hess = np.where(residual < 0, 2 * 10.0, 2.0)
return grad, hess
class MyXGBMAsymMSE(BaseCustomModel, XGBoostGBMModel):
"""Custom model class that re-uses DAI XGBoostGBMModel
The class inherits :
- BaseCustomModel that really is just a tag. It's there to make sure DAI knows it's a custom model and not
its inner XGBoost Model
- XGBoostGBMModel object so that the custom model inherits all the properties and methods, especially for params
# The loss is a regression loss
_regression = True
# The loss is not for classification
_binary = False
_multiclass = False # WIP
_mojo = True
# Give the display name and description that will be shown in the UI
_display_name = "MYXGBMAsymMSE"
_description = "XGBoost with custom asymetric loss/objective"
def set_default_params(self,
accuracy=None, time_tolerance=None, interpretability=None,
# First call the XGBoostGBMModel set_default_params
# This will input all model parameters just like DAI would do.
# 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=10, accuracy=10, interpretability=1,
train_shape=(1, 1), ncol_effective=1,
time_series=False, ensemble_level=0,
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
get_best=get_best, time_tolerance=time_tolerance, accuracy=accuracy,
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
self.params["objective"] = custom_asymmetric_objective
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