Partial Least Square Regression Constraint

Module for formulating simple Scikit-Learn Partial Least Squares models in a PySCIPOpt model.

pyscipopt_ml.sklearn.add_pls_regression_constr(scip_model, pls_regression, input_vars, output_vars=None, unique_naming_prefix='', **kwargs)

Formulate pls_regression in scip_model.

The formulation predicts the values of output_vars using input_vars according to pls_regression.

Parameters:

  • scip_model (PySCIPOpt Model) – The PySCIPOpt model where the predictor will be inserted.

  • pls_regression (sklearn.cross_decomposition.PLSRegression or) – sklearn.cross_decomposition.PLSCanonical The partial least squares model to insert.

  • input_vars (:list or dict) – Decision variables used as input for partial least squares regression in model.

  • output_vars (list or dict) – Decision variables used as output for partial least squares regression in model.

  • unique_naming_prefix (str, optional) – A unique naming prefix that is used before all variable and constraint names. This parameter is important if the SCIP model is later printed to file and many predictors are added to the same SCIP model.

Returns:

Object containing information about what was added to scip_model to formulate pls_regression.

Return type:

PLSRegressionConstr

Note

See add_predictor_constr for acceptable values for input_vars and output_vars

class pyscipopt_ml.sklearn.pls.PLSRegressionConstr(scip_model, predictor, input_vars, output_vars=None, unique_naming_prefix='', **kwargs)

Class to model trained sklearn.cross_decomposition.PLSRegression or sklearn.cross_decomposition.PLSCanonical with SCIP

Stores the changes to the SCIP Model for representing an instance into it. Inherits from AbstractPredictorConstr..

add_regression_constr()

Add the prediction constraints to SCIP.