def transform_optimization_config(
self,
optimization_config: OptimizationConfig,
fixed_features: ObservationFeatures,
) -> Any:
"""Applies transforms to given optimization config.
Args:
optimization_config: OptimizationConfig to transform.
fixed_features: features which should not be transformed.
Returns:
Transformed values. This could be e.g. a torch Tensor, depending
on the ModelBridge subclass.
"""
optimization_config = optimization_config.clone()
for t in self.transforms.values():
optimization_config = t.transform_optimization_config(
optimization_config=optimization_config,
modelbridge=self,
fixed_features=fixed_features,
)
return optimization_config
def testClone(self):
config1 = OptimizationConfig(
objective=self.objective, outcome_constraints=self.outcome_constraints
)
self.assertEqual(config1, config1.clone())
