add_lgbm_booster_constr

gurobi_ml.lightgbm.lgbm_regressor.add_lgbm_booster_constr(gp_model, lgbm_booster, input_vars, output_vars=None, epsilon=0.0, **kwargs)

Formulate lgbm_booster into gp_model.

The formulation predicts the values of output_vars using input_vars according to lgbm_regressor. See our User’s Guide for details on the mip formulation used.

Note that only “gbtree” regressors are supported at this point.

Parameters:

  • gp_model (Model) – The gurobipy model where the predictor should be inserted.

  • lgbm_booster (lightgbm.Booster) – The booster to insert as predictor.

  • input_vars (mvar_array_like) – Decision variables used as input for gradient boosting regressor in gp_model.

  • output_vars (mvar_array_like, optional) – Decision variables used as output for gradient boosting regressor in gp_model.

  • epsilon (float, optional) – Small value used to impose strict inequalities for splitting nodes in MIP formulations.

Returns:

Object containing information about what was added to gp_model to formulate gradient_boosting_regressor.

Return type:

LightGBMRegressorConstr

Notes

See add_predictor_constr for acceptable values for input_vars and output_vars

Also see gurobi_ml.sklearn.decision_tree_regressor.add_decision_tree_regressor_constr() for specific parameters to model decision tree estimators.

Raises:

ModelConfigurationError – If the booster is not of type “gbtree”.