AbstractPredictorConstr

class gurobi_ml.modeling.base_predictor_constr.AbstractPredictorConstr(gp_model, input_vars, output_vars=None, **kwargs)

Bases: ABC, _SubModel

Base class to store addtions for formulating predictor constraints

This class is the base class for formulating the various predictors supported by the package. It provides basic functionalities for storing the modeling structures (variables, constraints,…) that are added to the gurobipy models in order to formulate the predictor in a MIP.

It also provides simple functionalities to access the input and output variables and their results.

The implementation of the formulations of the various models is done in child classes. Depending on the type of the predictor, a class derived from this is returned by gurobi_ml.add_predictor_constr().

Warning

Users shouldn’t construct objects of this class or one of its derived classes directly. Those objects are returned by the gurobi_ml.add_predictor_constr() and other functions.

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)	Returns 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.

abstract get_error(eps)

Returns 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 model represented by this object.

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.