def generate_lower_bound_problem(
cls,
best_hyperparameters,
init_constraints,
best_model_state,
data,
di,
preset_model,
probeID=-1,
):
is_priv = is_lupi_feature(
di, data, best_model_state
) # Is it a lupi feature where we need additional candidate problems?
if not is_priv:
yield from super().generate_lower_bound_problem(
best_hyperparameters,
init_constraints,
best_model_state,
data,
di,
preset_model,
probeID=probeID,
)
else:
for sign in [1, -1]:
problem = cls(
di,
data,
best_hyperparameters,
init_constraints,
preset_model=preset_model,
best_model_state=best_model_state,
probeID=probeID,
)
problem.init_objective_LB(sign=sign)
problem.isLowerBound = True
yield problem
def generate_upper_bound_problem(
cls,
best_hyperparameters,
init_constraints,
best_model_state,
data,
di,
preset_model,
probeID=-1,
):
is_priv = is_lupi_feature(
di, data, best_model_state
) # Is it a lupi feature where we need additional candidate problems?
if not is_priv:
yield from super().generate_upper_bound_problem(
best_hyperparameters,
init_constraints,
best_model_state,
data,
di,
preset_model,
probeID=probeID,
)
else:
for sign, pos in product([1, -1], [True, False]):
problem = cls(
di,
data,
best_hyperparameters,
init_constraints,
preset_model=preset_model,
best_model_state=best_model_state,
probeID=probeID,
)
problem.init_objective_UB(sign=sign, pos=pos)
yield problem