API#

Main Function#

The main function provided by the package formulates a predictor in a Gurobi model. In most cases, it is the only function needed from the package.

Some formulations of the predictors can have additional options those are documented in the specific functions for each regression model.

add_predictor_constr(gp_model, predictor, ...)

Formulate predictor in gp_model.

`column_transformer`	Module for formulating a `sklearn.compose.ColumnTransformer` in a gurobipy model.
`decision_tree_regressor`	Module for formulating a `sklearn.tree.DecisionTreeRegressor` in a gurobipy model.
`gradient_boosting_regressor`	Module for formulating a `sklearn.ensemble.GradientBoostingRegressor` into a gurobipy model.
`linear_regression`	Module for formulating ordinary regression models into a gurobipy model.
`logistic_regression`	Module for formulating a `sklearn.linear_model.LogisticRegression` in a gurobipy model.
`mlpregressor`	Module for formulating a `sklearn.neural_network.MLPRegressor` in a gurobipy model.
`pipeline`	Module for formulating a `sklearn.pipeline.Pipeline` in a gurobipy model.
`pls_regression`	Module for formulating `sklearn.cross_decomposition.PLSRegression` in a gurobipy model.
`random_forest_regressor`	Module for formulating a `sklearn.ensemble.RandomForestRegressor` into a gurobipy model.
`preprocessing`	Implementation for using sklearn preprocessing object in a Guobi model.

Module for formulating a Keras model into a gurobipy model.

Module for formulating torch.nn.Sequential model in a gurobipy model.

Module for formulating a XGBoost gradient boosting regressor into a gurobipy model.

Module for formulating a LightGBM gradient boosting regressor into a gurobipy model.

Internal APIs