BaseSKlearnRegressionConstr#

class gurobi_ml.sklearn.base_regressions.BaseSKlearnRegressionConstr(gp_model, predictor, input_vars, output_vars=None, output_type='', **kwargs)#

Bases: SKgetter, AbstractPredictorConstr

Predict a Gurobi variable using a Linear Regression that takes another Gurobi matrix variable as input.

Attributes:

constrs: List of linear constraints added.
default_name: Default base name base used for automatic name generation.
genconstrs: List of general constraints added.
gp_model: Access gurobipy model this is a part of.
input: Input variables of embedded predictor.
input_values: Values for the input variables if a solution is known.
output: Output variables of embedded predictor.
output_values: Values for the output variables if a solution is known.
qconstrs: List of quadratic constraints added.
sos: List of SOS constraints added.
vars: List of variables added.

Methods

`get_error`([eps])	Return error in Gurobi's solution with respect to prediction from input.
`print_stats`([abbrev, file])	Print statistics on model additions stored by this class.
`remove`()	Remove from gp_model everything that was added to embed predictor.

get_error(eps=None)#

Return error in Gurobi’s solution with respect to prediction from input.

Returns:: error – Assuming that we have a solution for the input and output variables x, y. Returns the absolute value of the differences between predictor.predict(x) and y. Where predictor is the regression this object is modeling.
Return type:: ndarray of same shape as gurobi_ml.modeling.base_predictor_constr.AbstractPredictorConstr.output
Raises:: NoSolution – If the Gurobi model has no solution (either was not optimized or is infeasible).

print_stats(abbrev=False, file=None)#

Print statistics on model additions stored by this class.

This function prints detailed statistics on the variables and constraints that where added to the model.

Usually derived classes reimplement this function to provide more details about the structure of the additions (type of ML model, layers if it’s a neural network,…)

Parameters:: file (None, optional) – Text stream to which output should be redirected. By default sys.stdout.

remove()#: Remove from gp_model everything that was added to embed predictor.

property constrs#: List of linear constraints added.

property default_name#: Default base name base used for automatic name generation.

property genconstrs#: List of general constraints added.

property gp_model#: Access gurobipy model this is a part of.

property input#

Input variables of embedded predictor.

Returns:: output
Return type:: gurobipy MVar.

property input_values#

Values for the input variables if a solution is known.

Returns:: output_value
Return type:: ndarray or pandas dataframe with values
Raises:: NoSolution – If the Gurobi model has no solution (either was not optimized or is infeasible).

property output#

Output variables of embedded predictor.

Returns:: output
Return type:: gurobipy MVar.

property output_values#

Values for the output variables if a solution is known.

Returns:: output_value
Return type:: ndarray or pandas dataframe with values
Raises:: NoSolution – If the Gurobi model has no solution (either was not optimized or is infeasible).

property qconstrs#: List of quadratic constraints added.

property sos#: List of SOS constraints added.

property vars#: List of variables added.