def cost_sampler()

in botorch/acquisition/knowledge_gradient.py [0:0]

• 4 lines of code
• 7 McCabe index (conditional complexity)

    def cost_sampler(self):
        if self._cost_sampler is None:
            # Note: Using the deepcopy here is essential. Removing this poses a
            # problem if the base model and the cost model have a different number
            # of outputs or test points (this would be caused by expand), as this
            # would trigger re-sampling the base samples in the fantasy sampler.
            # By cloning the sampler here, the right thing will happen if the
            # the sizes are compatible, if they are not this will result in
            # samples being drawn using different base samples, but it will at
            # least avoid changing state of the fantasy sampler.
            self._cost_sampler = deepcopy(self.sampler)
        return self._cost_sampler