def _score_transform()

in level_replay/level_sampler.py [0:0]

• 24 lines of code
• 8 McCabe index (conditional complexity)

    def _score_transform(self, transform, temperature, scores):
        if transform == 'constant':
            weights = np.ones_like(scores)
        if transform == 'max':
            weights = np.zeros_like(scores)
            scores = scores[:]
            scores[self.unseen_seed_weights > 0] = -float('inf') # only argmax over seen levels
            argmax = np.random.choice(np.flatnonzero(np.isclose(scores, scores.max())))
            weights[argmax] = 1.
        elif transform == 'eps_greedy':
            weights = np.zeros_like(scores)
            weights[scores.argmax()] = 1. - self.eps
            weights += self.eps/len(self.seeds)
        elif transform == 'rank':
            temp = np.flip(scores.argsort())
            ranks = np.empty_like(temp)
            ranks[temp] = np.arange(len(temp)) + 1
            weights = 1/ranks ** (1./temperature)
        elif transform == 'power':
            eps = 0 if self.staleness_coef > 0 else 1e-3
            weights = (np.array(scores) + eps) ** (1./temperature)
        elif transform == 'softmax':
            weights = np.exp(np.array(scores)/temperature)

        return weights